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https://api.github.com/repos/huggingface/datasets/issues/6759 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6759/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6759/comments | https://api.github.com/repos/huggingface/datasets/issues/6759/events | https://github.com/huggingface/datasets/issues/6759 | 2,208,892,891 | I_kwDODunzps6DqQfb | 6,759 | Persistent multi-process Pool | {
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] | open | false | null | [] | null | 0 | "2024-03-26T17:35:25" | "2024-03-26T17:35:25" | null | NONE | null | ### Feature request
Running .map and filter functions with `num_procs` consecutively instantiates several multiprocessing pools iteratively.
As instantiating a Pool is very resource intensive it can be a bottleneck to performing iteratively filtering.
My ideas:
1. There should be an option to declare `persistent_workers` similar to pytorch DataLoader. Downside would be that would be complex to determine the correct resource allocation and deallocation of the pool. i.e. the dataset can outlive the utility of the pool.
2. Provide a pool as an argument. Downside would be the expertise required by the user. Upside, is that there is better resource management.
### Motivation
Is really slow to iteratively perform map and filter operations on a dataset.
### Your contribution
If approved I could integrate it. I would need to know what method would be most suitable to implement from the two options above. | {
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https://api.github.com/repos/huggingface/datasets/issues/6758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6758/comments | https://api.github.com/repos/huggingface/datasets/issues/6758/events | https://github.com/huggingface/datasets/issues/6758 | 2,208,494,302 | I_kwDODunzps6DovLe | 6,758 | Passing `sample_by` to `load_dataset` when loading text data does not work | {
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I have a dataset that consists of a bunch of text files, each representing an example. There is an undocumented `sample_by` argument for the `TextConfig` class that is used by `Text` to decide whether to split files into lines, paragraphs or take them whole. Passing `sample_by=“document”` to `load_dataset` results in files getting split into lines regardless. I have edited `src/datasets/packaged_modules/text/text.py` for myself to switch the default and it works fine.
As a side note, the `if-else` for `sample_by` will silently load an empty dataset if someone makes a typo in the argument, which is not ideal.
### Steps to reproduce the bug
1. Prepare data as a bunch of files in a directory.
2. Load that data via `load_dataset(“text”, data_files=<data_dir>/<files_glob>, …, sample_by=“document”)`.
3. Inspect the resultant dataset — every item should have the form of `{“text”: <a line from a file>}`.
### Expected behavior
`load_dataset(“text”, data_files=<data_dir>/<files_glob>, …, sample_by=“document”)` should result in a dataset with items of the form `{“text”: <one document>}`.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.0-1046-nvidia-x86_64-with-glibc2.35
- Python version: 3.11.8
- `huggingface_hub` version: 0.21.4
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | {
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"Thanks for reporting! We are working on a fix."
] |
https://api.github.com/repos/huggingface/datasets/issues/6757 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6757/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6757/comments | https://api.github.com/repos/huggingface/datasets/issues/6757/events | https://github.com/huggingface/datasets/pull/6757 | 2,206,280,340 | PR_kwDODunzps5qr7Li | 6,757 | Test disabling transformers containers in docs CI | {
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} | [] | open | false | null | [] | null | 3 | "2024-03-25T17:16:11" | "2024-03-26T13:33:36" | null | CONTRIBUTOR | null | Related to https://github.com/huggingface/doc-builder/pull/487 and [internal slack thread](https://huggingface.slack.com/archives/C04F8N7FQNL/p1711384899462349?thread_ts=1711041424.720769&cid=C04F8N7FQNL). There is now a `custom_container` option when building docs in CI. When set to `""` (instead of `"huggingface/transformers-doc-builder"` by default), we don't run the CI inside a container, therefore saving ~2min of download time. The plan is to test disabling the transformers container on a few "big" repo and if everything works correctly, we will stop making it the default container. More details on https://github.com/huggingface/doc-builder/pull/487.
cc @mishig25 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6757). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"On slack it was mentioned that it was actually slower for `datasets`, should we close this one or am I missing something ?",
"@lhoestq I converted to draft. Want to make some more tests and will let you know"
] |
https://api.github.com/repos/huggingface/datasets/issues/6756 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6756/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6756/comments | https://api.github.com/repos/huggingface/datasets/issues/6756/events | https://github.com/huggingface/datasets/issues/6756 | 2,205,557,725 | I_kwDODunzps6DdiPd | 6,756 | Support SQLite files? | {
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] | closed | false | null | [] | null | 3 | "2024-03-25T11:48:05" | "2024-03-26T16:09:32" | "2024-03-26T16:09:32" | CONTRIBUTOR | null | ### Feature request
Support loading a dataset from a SQLite file
https://huggingface.co/datasets/severo/test_iris_sqlite/tree/main
### Motivation
SQLite is a popular file format.
### Your contribution
See discussion on slack: https://huggingface.slack.com/archives/C04L6P8KNQ5/p1702481859117909 (internal)
In particular: a SQLite file can contain multiple tables, which might be matched to multiple configs. Maybe the detail of splits and configs should be defined in the README YAML, or use the same format as for ZIP files: `Iris.sqlite::Iris`.
See dataset here: https://huggingface.co/datasets/severo/test_iris_sqlite
Note: should we also support DuckDB files? | {
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"You can use `Dataset.from_sql(path_to_sql_file)` already. Though we haven't added the Sql dataset builder to the `_PACKAGED_DATASETS_MODULES` list or in `_EXTENSION_TO_MODULE` to map `.sqlite` to the Sql dataset builder\r\n\r\nThis would allow to load a dataset repository with a `.sqlite` file using `load_dataset` and enable the Dataset Viewer",
"Considering `Dataset.from_sql`'s (extremely) low usage, I don't think many users are interested in using this format for their datasets. Also, SQLite files are hard/impossible to stream efficiently and require custom logic to define splits/subsets, so IMO we shouldn't encourage people to use SQLite on the Hub.\r\n\r\n@severo Do you have some real-world examples of datasets published in this format?",
"No. Indeed, it seems better to explicitly not support sqlite"
] |
https://api.github.com/repos/huggingface/datasets/issues/6755 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6755/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6755/comments | https://api.github.com/repos/huggingface/datasets/issues/6755/events | https://github.com/huggingface/datasets/issues/6755 | 2,204,573,289 | I_kwDODunzps6DZx5p | 6,755 | Small typo on the documentation | {
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] | open | false | null | [] | null | 1 | "2024-03-24T21:47:52" | "2024-03-26T15:35:29" | null | NONE | null | ### Describe the bug
There is a small typo on https://github.com/huggingface/datasets/blob/d5468836fe94e8be1ae093397dd43d4a2503b926/src/datasets/dataset_dict.py#L938
It should be `caching is enabled`.
### Steps to reproduce the bug
Please visit
https://github.com/huggingface/datasets/blob/d5468836fe94e8be1ae093397dd43d4a2503b926/src/datasets/dataset_dict.py#L938
### Expected behavior
`caching is enabled`
### Environment info
- `datasets` version: 2.17.1
- Platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35
- Python version: 3.11.7
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
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"Thanks for reporting @fostiropoulos! I've edited your comment to fix the link to the problematic line.\r\n"
] |
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I didn't find a guideline on how to run the tests, so i just run the following steps to make sure that this bug is fixed.
1. `python test.py`,
2. then `HF_DATASETS_OFFLINE=1 python test.py`
The `test.py` is
```
import datasets
datasets.utils.logging.set_verbosity_info()
ds = datasets.load_dataset('izhx/STS17-debug')
print(ds)
ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')
print(ds)
```
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} | [] | open | false | null | [] | null | 2 | "2024-03-24T03:01:30" | "2024-03-27T00:55:31" | null | NONE | null | ### Describe the bug
When trying to run
```
import datasets
print(datasets.__version__)
```
It generates the following error
```
TypeError: expected string or bytes-like object
```
It looks like It cannot find the valid versions of `fsspec`
though fsspec version is fine when I checked Via command
```
import fsspec
print(fsspec.__version__)
# output: 2024.3.1
```
Detailed crash report
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[1], line 1
----> 1 import datasets
2 print(datasets.__version__)
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__ = "2.18.0"
---> 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:66
63 from multiprocess import Pool
64 from tqdm.contrib.concurrent import thread_map
---> 66 from . import config
67 from .arrow_reader import ArrowReader
68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File /opt/conda/lib/python3.10/site-packages/datasets/config.py:41
39 # Imports
40 DILL_VERSION = version.parse(importlib.metadata.version("dill"))
---> 41 FSSPEC_VERSION = version.parse(importlib.metadata.version("fsspec"))
42 PANDAS_VERSION = version.parse(importlib.metadata.version("pandas"))
43 PYARROW_VERSION = version.parse(importlib.metadata.version("pyarrow"))
File /opt/conda/lib/python3.10/site-packages/packaging/version.py:49, in parse(version)
43 """
44 Parse the given version string and return either a :class:`Version` object
45 or a :class:`LegacyVersion` object depending on if the given version is
46 a valid PEP 440 version or a legacy version.
47 """
48 try:
---> 49 return Version(version)
50 except InvalidVersion:
51 return LegacyVersion(version)
File /opt/conda/lib/python3.10/site-packages/packaging/version.py:264, in Version.__init__(self, version)
261 def __init__(self, version: str) -> None:
262
263 # Validate the version and parse it into pieces
--> 264 match = self._regex.search(version)
265 if not match:
266 raise InvalidVersion(f"Invalid version: '{version}'")
TypeError: expected string or bytes-like object
```
### Steps to reproduce the bug
1. run `!pip install -U datasets` on kaggle
2. check datasets is installed via
```
import datasets
print(datasets.__version__)
```
### Expected behavior
Expected to print datasets version, like `2.18.0`
### Environment info
Running on Kaggle, latest enviornment , here is the notebook https://www.kaggle.com/code/jtv199/mistrial-7b-part2 | {
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"I have the same problem \r\nIt seems that it only appears when you are using GPU \r\nIt seems to work fine with the 2.17 version though",
"Same here."
] |
https://api.github.com/repos/huggingface/datasets/issues/6752 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6752/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6752/comments | https://api.github.com/repos/huggingface/datasets/issues/6752/events | https://github.com/huggingface/datasets/issues/6752 | 2,204,043,839 | I_kwDODunzps6DXwo_ | 6,752 | Precision being changed from float16 to float32 unexpectedly | {
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} | [] | open | false | null | [] | null | 0 | "2024-03-23T20:53:56" | "2024-03-23T20:53:56" | null | NONE | null | ### Describe the bug
I'm loading a HuggingFace Dataset for images.
I'm running a preprocessing (map operation) step that runs a few operations, one of them being conversion to float16. The Dataset features also say that the 'img' is of type float16. Whenever I take an image from that HuggingFace Dataset instance, the type turns out to be float32.
### Steps to reproduce the bug
```python
import torchvision.transforms.v2 as transforms
from datasets import load_dataset
dataset = load_dataset('cifar10', split='test')
dataset = dataset.with_format("torch")
data_transform = transforms.Compose([transforms.Resize((32, 32)),
transforms.ToDtype(torch.float16, scale=True),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
def _preprocess(examples):
# Permutes from (BS x H x W x C) to (BS x C x H x W)
images = torch.permute(examples['img'], (0, 3, 2, 1))
examples['img'] = data_transform(images)
return examples
dataset = dataset.map(_preprocess, batched=True, batch_size=8)
```
Now at this point the dataset.features are showing float16 which is great because that's what I want.
```python
print(data_loader.features['img'])
Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float16', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None)
```
But when I try to sample an image from this dataloader; I'm getting a float32 image, when I'm expecting float16:
```python
print(next(iter(data_loader))['img'].dtype)
torch.float32
```
### Expected behavior
I'm expecting the images loaded after the transformation to stay in float16.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.9
- `huggingface_hub` version: 0.21.4
- PyArrow version: 14.0.2
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6751 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6751/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6751/comments | https://api.github.com/repos/huggingface/datasets/issues/6751/events | https://github.com/huggingface/datasets/pull/6751 | 2,203,951,501 | PR_kwDODunzps5qkKLH | 6,751 | Use 'with' operator for some download functions | {
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} | [] | closed | false | null | [] | null | 2 | "2024-03-23T16:32:08" | "2024-03-26T00:40:57" | "2024-03-26T00:40:57" | NONE | null | Some functions in `streaming_download_manager.py` are not closing the file they open which lead to `Unclosed file` warnings in our code. This fixes a few of them. | {
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} | true | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6751). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I was mistaken on the intent of those functions, closing the PR."
] |
https://api.github.com/repos/huggingface/datasets/issues/6750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6750/comments | https://api.github.com/repos/huggingface/datasets/issues/6750/events | https://github.com/huggingface/datasets/issues/6750 | 2,203,590,658 | I_kwDODunzps6DWCAC | 6,750 | `load_dataset` requires a network connection for local download? | {
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} | [] | open | false | null | [] | null | 2 | "2024-03-23T01:06:32" | "2024-03-24T04:04:43" | null | NONE | null | ### Describe the bug
Hi all - I see that in the past a network dependency has been mistakenly introduced into `load_dataset` even for local loads. Is it possible this has happened again?
### Steps to reproduce the bug
```
>>> import datasets
>>> datasets.load_dataset("hh-rlhf")
Repo card metadata block was not found. Setting CardData to empty.
*hangs bc i'm firewalled*
````
stack trace from ctrl-c:
```
^CTraceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/load.py", line 2582, in load_dataset
builder_instance.download_and_prepare(
output_path = get_from_cache( [0/122]
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 532, in get_from_cache
response = http_head(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 419, in http_head
response = _request_with_retry(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 304, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/adapters.py", line 487, in send
resp = conn.urlopen(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 363, in connect
self.sock = conn = self._new_conn()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
```
### Expected behavior
loads the dataset
### Environment info
```
> pip show datasets
Name: datasets
Version: 2.18.0
```
Python 3.10.2 | {
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"Are you using `HF_DATASETS_OFFLINE=1` ?",
"> Are you using `HF_DATASETS_OFFLINE=1` ?\r\n\r\nThis doesn't work for me. `datasets=2.18.0`\r\n\r\n`test.py`:\r\n```\r\nimport datasets\r\n\r\ndatasets.utils.logging.set_verbosity_info()\r\n\r\nds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n\r\nprint(ds)\r\n```\r\n\r\nrun `python test.py`\r\n```\r\nGenerating dataset afqmc (/home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4)\r\nDownloading and preparing dataset afqmc/default to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nhf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet not found in cache or force_download set to True, downloading to /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b.incomplete\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 240k/240k [00:01<00:00, 178kB/s]\r\nstoring hf://datasets/C-MTEB/AFQMC@b44c3b011063adb25877c13823db83bb193913c4/data/validation-00000-of-00001-b8fc393b5ddedac7.parquet in cache at /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\ncreating metadata file for /home/data/.cache/huggingface/datasets/downloads/78949f93104662359f4f3d5a2f7ec1ae37af5a5af44420a51212ea08c0be966b\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nGenerating test split\r\nGenerating test split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3861/3861 [00:00<00:00, 3972.00 examples/s]\r\nGenerating train split\r\nGenerating train split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 34334/34334 [00:00<00:00, 34355.50 examples/s]\r\nGenerating validation split\r\nGenerating validation split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4316/4316 [00:00<00:00, 4477.00 examples/s]\r\nAll the splits matched successfully.\r\nDataset afqmc downloaded and prepared to /home/data/.cache/huggingface/datasets/C-MTEB___afqmc/default/0.0.0/b44c3b011063adb25877c13823db83bb193913c4. Subsequent calls will reuse this data.\r\nDatasetDict({\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 3861\r\n })\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 34334\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'score', 'idx'],\r\n num_rows: 4316\r\n })\r\n})\r\n```\r\n\r\nThen run `HF_DATASETS_OFFLINE=1 python test.py`\r\n```\r\nTraceback (most recent call last):\r\n File \"test.py\", line 9, in <module>\r\n ds = datasets.load_dataset('C-MTEB/AFQMC', revision='b44c3b011063adb25877c13823db83bb193913c4')\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2556, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 2228, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/dev/shm/tmp_env/lib/python3.10/site-packages/datasets/load.py\", line 1871, in dataset_module_factory\r\n raise ConnectionError(f\"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}\") from None\r\nConnectionError: Couldn't reach the Hugging Face Hub for dataset 'C-MTEB/AFQMC': Offline mode is enabled.\r\n```\r\n\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6749 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6749/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6749/comments | https://api.github.com/repos/huggingface/datasets/issues/6749/events | https://github.com/huggingface/datasets/pull/6749 | 2,202,310,116 | PR_kwDODunzps5qeoSk | 6,749 | Fix fsspec tqdm callback | {
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} | [] | closed | false | null | [] | null | 2 | "2024-03-22T11:44:11" | "2024-03-22T14:51:45" | "2024-03-22T14:45:39" | MEMBER | null | Following changes at https://github.com/fsspec/filesystem_spec/pull/1497 for `fsspec>=2024.2.0` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6749). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005017 / 0.011353 (-0.006336) | 0.002958 / 0.011008 (-0.008050) | 0.063455 / 0.038508 (0.024946) | 0.028206 / 0.023109 (0.005096) | 0.230884 / 0.275898 (-0.045014) | 0.252688 / 0.323480 (-0.070792) | 0.002995 / 0.007986 (-0.004991) | 0.002613 / 0.004328 (-0.001716) | 0.046477 / 0.004250 (0.042226) | 0.040662 / 0.037052 (0.003609) | 0.241824 / 0.258489 (-0.016665) | 0.269063 / 0.293841 (-0.024778) | 0.027336 / 0.128546 (-0.101210) | 0.010614 / 0.075646 (-0.065032) | 0.216087 / 0.419271 (-0.203184) | 0.035667 / 0.043533 (-0.007866) | 0.238657 / 0.255139 (-0.016482) | 0.253433 / 0.283200 (-0.029767) | 0.017433 / 0.141683 (-0.124250) | 1.120856 / 1.452155 (-0.331299) | 1.157415 / 1.492716 (-0.335302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088028 / 0.018006 (0.070022) | 0.277368 / 0.000490 (0.276878) | 0.000204 / 0.000200 (0.000004) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017956 / 0.037411 (-0.019455) | 0.061061 / 0.014526 (0.046535) | 0.073323 / 0.176557 (-0.103234) | 0.119254 / 0.737135 (-0.617881) | 0.074308 / 0.296338 (-0.222031) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285118 / 0.215209 (0.069908) | 2.785796 / 2.077655 (0.708142) | 1.476436 / 1.504120 (-0.027684) | 1.356505 / 1.541195 (-0.184690) | 1.362505 / 1.468490 (-0.105985) | 0.554064 / 4.584777 (-4.030713) | 2.395774 / 3.745712 (-1.349938) | 2.713703 / 5.269862 (-2.556159) | 1.701020 / 4.565676 (-2.864657) | 0.062370 / 0.424275 (-0.361905) | 0.004944 / 0.007607 (-0.002663) | 0.327948 / 0.226044 (0.101904) | 3.243739 / 2.268929 (0.974811) | 1.803881 / 55.444624 (-53.640743) | 1.551635 / 6.876477 (-5.324841) | 1.560627 / 2.142072 (-0.581446) | 0.628187 / 4.805227 (-4.177040) | 0.115824 / 6.500664 (-6.384840) | 0.041655 / 0.075469 (-0.033814) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968797 / 1.841788 (-0.872991) | 11.220905 / 8.074308 (3.146597) | 9.322584 / 10.191392 (-0.868808) | 0.139629 / 0.680424 (-0.540795) | 0.013823 / 0.534201 (-0.520378) | 0.286700 / 0.579283 (-0.292583) | 0.263517 / 0.434364 (-0.170847) | 0.341264 / 0.540337 (-0.199074) | 0.418834 / 1.386936 (-0.968102) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005404 / 0.011353 (-0.005949) | 0.003630 / 0.011008 (-0.007378) | 0.048977 / 0.038508 (0.010469) | 0.029980 / 0.023109 (0.006871) | 0.274671 / 0.275898 (-0.001227) | 0.295671 / 0.323480 (-0.027808) | 0.004230 / 0.007986 (-0.003756) | 0.002656 / 0.004328 (-0.001672) | 0.048603 / 0.004250 (0.044353) | 0.044323 / 0.037052 (0.007271) | 0.286499 / 0.258489 (0.028010) | 0.313199 / 0.293841 (0.019358) | 0.030079 / 0.128546 (-0.098468) | 0.010480 / 0.075646 (-0.065166) | 0.058226 / 0.419271 (-0.361045) | 0.054920 / 0.043533 (0.011387) | 0.274921 / 0.255139 (0.019783) | 0.296559 / 0.283200 (0.013360) | 0.019164 / 0.141683 (-0.122519) | 1.154703 / 1.452155 (-0.297452) | 1.207015 / 1.492716 (-0.285701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089368 / 0.018006 (0.071362) | 0.301196 / 0.000490 (0.300706) | 0.000208 / 0.000200 (0.000008) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021355 / 0.037411 (-0.016056) | 0.074688 / 0.014526 (0.060162) | 0.085840 / 0.176557 (-0.090716) | 0.125784 / 0.737135 (-0.611351) | 0.087103 / 0.296338 (-0.209235) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296727 / 0.215209 (0.081518) | 2.884922 / 2.077655 (0.807267) | 1.586515 / 1.504120 (0.082395) | 1.474417 / 1.541195 (-0.066777) | 1.492105 / 1.468490 (0.023615) | 0.570016 / 4.584777 (-4.014761) | 2.435760 / 3.745712 (-1.309952) | 2.657999 / 5.269862 (-2.611863) | 1.740160 / 4.565676 (-2.825516) | 0.063743 / 0.424275 (-0.360532) | 0.005048 / 0.007607 (-0.002559) | 0.341279 / 0.226044 (0.115235) | 3.396185 / 2.268929 (1.127256) | 1.952825 / 55.444624 (-53.491800) | 1.676669 / 6.876477 (-5.199808) | 1.773158 / 2.142072 (-0.368915) | 0.650664 / 4.805227 (-4.154563) | 0.116815 / 6.500664 (-6.383849) | 0.040813 / 0.075469 (-0.034656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999836 / 1.841788 (-0.841952) | 11.854540 / 8.074308 (3.780232) | 10.245516 / 10.191392 (0.054124) | 0.141235 / 0.680424 (-0.539189) | 0.015562 / 0.534201 (-0.518639) | 0.287556 / 0.579283 (-0.291727) | 0.274946 / 0.434364 (-0.159418) | 0.324652 / 0.540337 (-0.215685) | 0.449204 / 1.386936 (-0.937733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed2b406d045349dad16738985c947fe743260710 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6748/comments | https://api.github.com/repos/huggingface/datasets/issues/6748/events | https://github.com/huggingface/datasets/issues/6748 | 2,201,517,348 | I_kwDODunzps6DOH0k | 6,748 | Strange slicing behavior | {
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} | [] | open | false | null | [] | null | 1 | "2024-03-22T01:49:13" | "2024-03-22T16:43:57" | null | NONE | null | ### Describe the bug
I have loaded a dataset, and then slice first 300 samples using `:` ops, however, the resulting dataset is not expected, as the output below:
```bash
len(dataset)=1050324
len(dataset[:300])=2
len(dataset[0:300])=2
len(dataset.select(range(300)))=300
```
### Steps to reproduce the bug
load a dataset then:
```bash
dataset = load_from_disk(args.train_data_dir)
print(f"{len(dataset)=}", flush=True)
print(f"{len(dataset[:300])=}", flush=True)
print(f"{len(dataset[0:300])=}", flush=True)
print(f"{len(dataset.select(range(300)))=}", flush=True)
```
### Expected behavior
```bash
len(dataset)=1050324
len(dataset[:300])=300
len(dataset[0:300])=300
len(dataset.select(range(300)))=300
```
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- `huggingface_hub` version: 0.20.2
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0 | {
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"As explained in the [docs](https://huggingface.co/docs/datasets/v2.18.0/en/access#slicing), slicing a `Dataset` returns a dictionary that maps its column names to their values. So, `len(dataset[:300])=2` is expected, assuming your dataset has 2 columns (the returned dict has 2 keys, but each value in the dict has 300 items).\r\n` "
] |
https://api.github.com/repos/huggingface/datasets/issues/6747 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6747/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6747/comments | https://api.github.com/repos/huggingface/datasets/issues/6747/events | https://github.com/huggingface/datasets/pull/6747 | 2,201,219,384 | PR_kwDODunzps5qa5L- | 6,747 | chore(deps): bump fsspec | {
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} | [] | closed | false | null | [] | null | 2 | "2024-03-21T21:25:49" | "2024-03-22T16:40:15" | "2024-03-22T16:28:40" | CONTRIBUTOR | null | There were a few fixes released recently, some DVC ecosystem packages require newer version of `fsspec`. | {
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} | true | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6747). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005129 / 0.011353 (-0.006224) | 0.003788 / 0.011008 (-0.007220) | 0.063456 / 0.038508 (0.024948) | 0.029079 / 0.023109 (0.005969) | 0.237228 / 0.275898 (-0.038670) | 0.260554 / 0.323480 (-0.062926) | 0.003090 / 0.007986 (-0.004895) | 0.002730 / 0.004328 (-0.001599) | 0.049040 / 0.004250 (0.044789) | 0.042432 / 0.037052 (0.005380) | 0.256954 / 0.258489 (-0.001535) | 0.285912 / 0.293841 (-0.007929) | 0.027568 / 0.128546 (-0.100978) | 0.010402 / 0.075646 (-0.065245) | 0.206773 / 0.419271 (-0.212499) | 0.035381 / 0.043533 (-0.008152) | 0.243147 / 0.255139 (-0.011992) | 0.259419 / 0.283200 (-0.023781) | 0.019503 / 0.141683 (-0.122180) | 1.145537 / 1.452155 (-0.306618) | 1.204070 / 1.492716 (-0.288646) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092298 / 0.018006 (0.074291) | 0.300042 / 0.000490 (0.299553) | 0.000236 / 0.000200 (0.000036) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018624 / 0.037411 (-0.018788) | 0.063832 / 0.014526 (0.049306) | 0.075849 / 0.176557 (-0.100707) | 0.120919 / 0.737135 (-0.616216) | 0.075878 / 0.296338 (-0.220461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275545 / 0.215209 (0.060336) | 2.706004 / 2.077655 (0.628349) | 1.406398 / 1.504120 (-0.097722) | 1.287154 / 1.541195 (-0.254041) | 1.298278 / 1.468490 (-0.170212) | 0.559763 / 4.584777 (-4.025014) | 2.434104 / 3.745712 (-1.311608) | 2.786338 / 5.269862 (-2.483523) | 1.720951 / 4.565676 (-2.844726) | 0.062082 / 0.424275 (-0.362193) | 0.004931 / 0.007607 (-0.002676) | 0.329998 / 0.226044 (0.103954) | 3.222105 / 2.268929 (0.953176) | 1.777539 / 55.444624 (-53.667085) | 1.533845 / 6.876477 (-5.342632) | 1.520357 / 2.142072 (-0.621715) | 0.638850 / 4.805227 (-4.166377) | 0.116718 / 6.500664 (-6.383946) | 0.042215 / 0.075469 (-0.033254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962791 / 1.841788 (-0.878997) | 11.509889 / 8.074308 (3.435581) | 9.507676 / 10.191392 (-0.683716) | 0.140780 / 0.680424 (-0.539644) | 0.014187 / 0.534201 (-0.520014) | 0.286363 / 0.579283 (-0.292920) | 0.263316 / 0.434364 (-0.171048) | 0.322099 / 0.540337 (-0.218239) | 0.415602 / 1.386936 (-0.971334) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005175 / 0.011353 (-0.006178) | 0.003631 / 0.011008 (-0.007377) | 0.050277 / 0.038508 (0.011769) | 0.031879 / 0.023109 (0.008770) | 0.269966 / 0.275898 (-0.005933) | 0.297229 / 0.323480 (-0.026251) | 0.004278 / 0.007986 (-0.003707) | 0.002936 / 0.004328 (-0.001393) | 0.048686 / 0.004250 (0.044436) | 0.044262 / 0.037052 (0.007209) | 0.284578 / 0.258489 (0.026089) | 0.313681 / 0.293841 (0.019840) | 0.029064 / 0.128546 (-0.099482) | 0.010700 / 0.075646 (-0.064946) | 0.058366 / 0.419271 (-0.360905) | 0.051341 / 0.043533 (0.007809) | 0.271262 / 0.255139 (0.016123) | 0.290791 / 0.283200 (0.007591) | 0.019044 / 0.141683 (-0.122639) | 1.149514 / 1.452155 (-0.302641) | 1.209277 / 1.492716 (-0.283439) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094879 / 0.018006 (0.076872) | 0.302196 / 0.000490 (0.301707) | 0.000217 / 0.000200 (0.000018) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021715 / 0.037411 (-0.015696) | 0.075122 / 0.014526 (0.060596) | 0.087393 / 0.176557 (-0.089164) | 0.125583 / 0.737135 (-0.611553) | 0.088722 / 0.296338 (-0.207617) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295158 / 0.215209 (0.079949) | 2.930208 / 2.077655 (0.852553) | 1.590197 / 1.504120 (0.086077) | 1.459038 / 1.541195 (-0.082156) | 1.471690 / 1.468490 (0.003200) | 0.570279 / 4.584777 (-4.014498) | 2.456971 / 3.745712 (-1.288741) | 2.675315 / 5.269862 (-2.594547) | 1.750122 / 4.565676 (-2.815554) | 0.062905 / 0.424275 (-0.361370) | 0.005118 / 0.007607 (-0.002489) | 0.344263 / 0.226044 (0.118219) | 3.472460 / 2.268929 (1.203532) | 1.931707 / 55.444624 (-53.512917) | 1.658537 / 6.876477 (-5.217939) | 1.785794 / 2.142072 (-0.356278) | 0.637149 / 4.805227 (-4.168078) | 0.115838 / 6.500664 (-6.384826) | 0.040771 / 0.075469 (-0.034698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.002869 / 1.841788 (-0.838919) | 12.048825 / 8.074308 (3.974517) | 10.407979 / 10.191392 (0.216587) | 0.150300 / 0.680424 (-0.530124) | 0.015299 / 0.534201 (-0.518902) | 0.286277 / 0.579283 (-0.293006) | 0.312186 / 0.434364 (-0.122178) | 0.322633 / 0.540337 (-0.217704) | 0.438431 / 1.386936 (-0.948505) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d5468836fe94e8be1ae093397dd43d4a2503b926 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6746 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6746/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6746/comments | https://api.github.com/repos/huggingface/datasets/issues/6746/events | https://github.com/huggingface/datasets/issues/6746 | 2,198,993,949 | I_kwDODunzps6DEfwd | 6,746 | ExpectedMoreSplits error when loading C4 dataset | {
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} | [] | open | false | null | [] | null | 1 | "2024-03-21T02:53:04" | "2024-03-21T14:06:08" | null | NONE | null | ### Describe the bug
I encounter bug when running the example command line
```python
python main.py \
--model decapoda-research/llama-7b-hf \
--prune_method wanda \
--sparsity_ratio 0.5 \
--sparsity_type unstructured \
--save out/llama_7b/unstructured/wanda/
```
The bug occurred at these lines of code (when loading c4 dataset)
```python
traindata = load_dataset('allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')
valdata = load_dataset('allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')
```
The error message states:
```
raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits)))
datasets.utils.info_utils.ExpectedMoreSplits: {'validation'}
```
### Steps to reproduce the bug
1. I encounter bug when running the example command line
### Expected behavior
The error message states:
```
raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits)))
datasets.utils.info_utils.ExpectedMoreSplits: {'validation'}
```
### Environment info
I'm using cuda 12.4, so I use ```pip install pytorch``` instead of conda provided in install.md
Also, I've tried another environment using the same commands in install.md, but the same bug occured | {
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"Hi ! We updated the `allenai/c4` repository to allow people to specify which language to load easily (the the [c4 dataset page](https://huggingface.co/datasets/allenai/c4))\r\n\r\nTo fix this issue you can update `datasets` and remove the mention of the legacy configuration name \"allenai--c4\":\r\n\r\n```python\r\ntraindata = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')\r\nvaldata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')\r\n```"
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https://api.github.com/repos/huggingface/datasets/issues/6745 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6745/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6745/comments | https://api.github.com/repos/huggingface/datasets/issues/6745/events | https://github.com/huggingface/datasets/issues/6745 | 2,198,541,732 | I_kwDODunzps6DCxWk | 6,745 | Scraping the whole of github including private repos is bad; kindly stop | {
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] | closed | false | null | [] | null | 1 | "2024-03-20T20:54:06" | "2024-03-21T12:28:04" | "2024-03-21T10:24:56" | NONE | null | ### Feature request
https://github.com/bigcode-project/opt-out-v2 - opt out is not consent. kindly quit this ridiculous nonsense.
### Motivation
[EDITED: insults not tolerated]
### Your contribution
[EDITED: insults not tolerated] | {
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"It's not twitter here"
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https://api.github.com/repos/huggingface/datasets/issues/6744 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6744/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6744/comments | https://api.github.com/repos/huggingface/datasets/issues/6744/events | https://github.com/huggingface/datasets/issues/6744 | 2,197,910,168 | I_kwDODunzps6DAXKY | 6,744 | Option to disable file locking | {
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] | open | false | null | [] | null | 0 | "2024-03-20T15:59:45" | "2024-03-20T15:59:45" | null | NONE | null | ### Feature request
Commands such as `load_dataset` creates file locks with `filelock.FileLock`. It would be good if there was a way to disable this.
### Motivation
File locking doesn't work on all file-systems (in my case NFS mounted Weka). If the `cache_dir` only had small files then it would be possible to point to local disk and the problem would be solved. However, as cache_dir is both where the small info files are written and the processed datasets are put this isn't a feasible solution.
Considering https://github.com/huggingface/datasets/issues/6395 I still do think this is something that belongs in HuggingFace. The possibility to control packages separately is valuable. It might be that a user has their dataset on a file-system that doesn't support file-locking while they are using file locking on local disk to control some other type of access.
### Your contribution
My suggested solution:
```
diff --git a/src/datasets/utils/_filelock.py b/src/datasets/utils/_filelock.py
index 19620e6e..58f41a02 100644
--- a/src/datasets/utils/_filelock.py
+++ b/src/datasets/utils/_filelock.py
@@ -18,11 +18,15 @@
import os
from filelock import FileLock as FileLock_
-from filelock import UnixFileLock
+from filelock import SoftFileLock, UnixFileLock
from filelock import __version__ as _filelock_version
from packaging import version
+if os.getenv('HF_USE_SOFTFILELOCK', 'false').lower() in ('true', '1'):
+ FileLock_ = SoftFileLock
+
+
class FileLock(FileLock_):
"""
A `filelock.FileLock` initializer that handles long paths.
```
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} | [] | closed | false | null | [] | null | 2 | "2024-03-19T16:54:22" | "2024-03-19T20:11:29" | "2024-03-19T20:05:19" | CONTRIBUTOR | null | Fix #6738 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6743). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005013 / 0.011353 (-0.006340) | 0.003228 / 0.011008 (-0.007780) | 0.062763 / 0.038508 (0.024255) | 0.028937 / 0.023109 (0.005828) | 0.240777 / 0.275898 (-0.035121) | 0.266972 / 0.323480 (-0.056508) | 0.003073 / 0.007986 (-0.004913) | 0.002769 / 0.004328 (-0.001560) | 0.049265 / 0.004250 (0.045015) | 0.042061 / 0.037052 (0.005009) | 0.261714 / 0.258489 (0.003225) | 0.284896 / 0.293841 (-0.008944) | 0.027717 / 0.128546 (-0.100829) | 0.010430 / 0.075646 (-0.065216) | 0.209022 / 0.419271 (-0.210249) | 0.035941 / 0.043533 (-0.007591) | 0.246849 / 0.255139 (-0.008290) | 0.263205 / 0.283200 (-0.019994) | 0.019489 / 0.141683 (-0.122193) | 1.102595 / 1.452155 (-0.349559) | 1.170493 / 1.492716 (-0.322223) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093611 / 0.018006 (0.075604) | 0.302041 / 0.000490 (0.301551) | 0.000223 / 0.000200 (0.000023) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018720 / 0.037411 (-0.018692) | 0.062199 / 0.014526 (0.047673) | 0.074888 / 0.176557 (-0.101669) | 0.120184 / 0.737135 (-0.616951) | 0.076756 / 0.296338 (-0.219583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287484 / 0.215209 (0.072275) | 2.787777 / 2.077655 (0.710123) | 1.488957 / 1.504120 (-0.015163) | 1.362678 / 1.541195 (-0.178517) | 1.364571 / 1.468490 (-0.103919) | 0.563139 / 4.584777 (-4.021638) | 2.422224 / 3.745712 (-1.323488) | 2.798011 / 5.269862 (-2.471850) | 1.751159 / 4.565676 (-2.814517) | 0.062740 / 0.424275 (-0.361536) | 0.004918 / 0.007607 (-0.002689) | 0.338285 / 0.226044 (0.112240) | 3.316012 / 2.268929 (1.047083) | 1.845975 / 55.444624 (-53.598650) | 1.553187 / 6.876477 (-5.323290) | 1.564582 / 2.142072 (-0.577490) | 0.645987 / 4.805227 (-4.159240) | 0.118216 / 6.500664 (-6.382448) | 0.041243 / 0.075469 (-0.034226) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970265 / 1.841788 (-0.871522) | 11.783152 / 8.074308 (3.708844) | 9.516584 / 10.191392 (-0.674808) | 0.148086 / 0.680424 (-0.532338) | 0.013689 / 0.534201 (-0.520512) | 0.289657 / 0.579283 (-0.289626) | 0.265966 / 0.434364 (-0.168398) | 0.328483 / 0.540337 (-0.211854) | 0.433544 / 1.386936 (-0.953392) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005235 / 0.011353 (-0.006118) | 0.003515 / 0.011008 (-0.007493) | 0.049484 / 0.038508 (0.010976) | 0.029264 / 0.023109 (0.006154) | 0.278518 / 0.275898 (0.002620) | 0.298948 / 0.323480 (-0.024532) | 0.004308 / 0.007986 (-0.003678) | 0.002751 / 0.004328 (-0.001577) | 0.048952 / 0.004250 (0.044701) | 0.045379 / 0.037052 (0.008327) | 0.292633 / 0.258489 (0.034144) | 0.319405 / 0.293841 (0.025564) | 0.030201 / 0.128546 (-0.098345) | 0.010657 / 0.075646 (-0.064990) | 0.057842 / 0.419271 (-0.361430) | 0.053359 / 0.043533 (0.009826) | 0.281136 / 0.255139 (0.025997) | 0.295388 / 0.283200 (0.012188) | 0.018786 / 0.141683 (-0.122897) | 1.187181 / 1.452155 (-0.264974) | 1.198394 / 1.492716 (-0.294323) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093861 / 0.018006 (0.075855) | 0.304019 / 0.000490 (0.303529) | 0.000220 / 0.000200 (0.000020) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021582 / 0.037411 (-0.015829) | 0.075381 / 0.014526 (0.060855) | 0.087886 / 0.176557 (-0.088671) | 0.125078 / 0.737135 (-0.612057) | 0.089339 / 0.296338 (-0.206999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295797 / 0.215209 (0.080588) | 2.912021 / 2.077655 (0.834367) | 1.592191 / 1.504120 (0.088071) | 1.471270 / 1.541195 (-0.069925) | 1.475535 / 1.468490 (0.007045) | 0.564114 / 4.584777 (-4.020663) | 2.442882 / 3.745712 (-1.302830) | 2.679433 / 5.269862 (-2.590428) | 1.752097 / 4.565676 (-2.813579) | 0.062748 / 0.424275 (-0.361527) | 0.005068 / 0.007607 (-0.002539) | 0.345554 / 0.226044 (0.119509) | 3.456929 / 2.268929 (1.188000) | 1.962781 / 55.444624 (-53.481844) | 1.688313 / 6.876477 (-5.188164) | 1.817392 / 2.142072 (-0.324681) | 0.639588 / 4.805227 (-4.165639) | 0.116148 / 6.500664 (-6.384516) | 0.040851 / 0.075469 (-0.034618) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.009852 / 1.841788 (-0.831936) | 12.031749 / 8.074308 (3.957440) | 10.305107 / 10.191392 (0.113715) | 0.132960 / 0.680424 (-0.547464) | 0.014779 / 0.534201 (-0.519422) | 0.288903 / 0.579283 (-0.290381) | 0.275417 / 0.434364 (-0.158947) | 0.322628 / 0.540337 (-0.217709) | 0.445060 / 1.386936 (-0.941876) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f234fce40d5ffc96fac5198d8cc89817970d87ee \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6742 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6742/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6742/comments | https://api.github.com/repos/huggingface/datasets/issues/6742/events | https://github.com/huggingface/datasets/pull/6742 | 2,195,134,854 | PR_kwDODunzps5qGSfG | 6,742 | Fix missing download_config in get_data_patterns | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6742). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005394 / 0.011353 (-0.005959) | 0.003780 / 0.011008 (-0.007228) | 0.063459 / 0.038508 (0.024951) | 0.028883 / 0.023109 (0.005774) | 0.239159 / 0.275898 (-0.036739) | 0.258123 / 0.323480 (-0.065357) | 0.003134 / 0.007986 (-0.004851) | 0.003452 / 0.004328 (-0.000876) | 0.049255 / 0.004250 (0.045005) | 0.042727 / 0.037052 (0.005675) | 0.257387 / 0.258489 (-0.001102) | 0.280762 / 0.293841 (-0.013079) | 0.027921 / 0.128546 (-0.100625) | 0.010867 / 0.075646 (-0.064779) | 0.207878 / 0.419271 (-0.211393) | 0.036003 / 0.043533 (-0.007530) | 0.247457 / 0.255139 (-0.007682) | 0.260231 / 0.283200 (-0.022969) | 0.019741 / 0.141683 (-0.121942) | 1.143645 / 1.452155 (-0.308510) | 1.188789 / 1.492716 (-0.303927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092065 / 0.018006 (0.074059) | 0.286021 / 0.000490 (0.285531) | 0.000220 / 0.000200 (0.000020) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018934 / 0.037411 (-0.018477) | 0.062474 / 0.014526 (0.047949) | 0.073384 / 0.176557 (-0.103172) | 0.121276 / 0.737135 (-0.615860) | 0.077792 / 0.296338 (-0.218546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285352 / 0.215209 (0.070143) | 2.783110 / 2.077655 (0.705456) | 1.487983 / 1.504120 (-0.016137) | 1.364264 / 1.541195 (-0.176930) | 1.388757 / 1.468490 (-0.079733) | 0.568347 / 4.584777 (-4.016430) | 2.402451 / 3.745712 (-1.343261) | 2.835577 / 5.269862 (-2.434285) | 1.754853 / 4.565676 (-2.810824) | 0.063355 / 0.424275 (-0.360920) | 0.005010 / 0.007607 (-0.002598) | 0.332061 / 0.226044 (0.106016) | 3.287121 / 2.268929 (1.018193) | 1.829520 / 55.444624 (-53.615104) | 1.542669 / 6.876477 (-5.333808) | 1.560679 / 2.142072 (-0.581393) | 0.642371 / 4.805227 (-4.162856) | 0.118636 / 6.500664 (-6.382028) | 0.042262 / 0.075469 (-0.033207) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984803 / 1.841788 (-0.856985) | 11.578044 / 8.074308 (3.503735) | 9.383428 / 10.191392 (-0.807964) | 0.141367 / 0.680424 (-0.539057) | 0.014047 / 0.534201 (-0.520154) | 0.291505 / 0.579283 (-0.287778) | 0.270199 / 0.434364 (-0.164165) | 0.329874 / 0.540337 (-0.210463) | 0.429386 / 1.386936 (-0.957550) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005322 / 0.011353 (-0.006031) | 0.004023 / 0.011008 (-0.006986) | 0.050126 / 0.038508 (0.011618) | 0.029937 / 0.023109 (0.006828) | 0.275985 / 0.275898 (0.000087) | 0.297965 / 0.323480 (-0.025515) | 0.004429 / 0.007986 (-0.003557) | 0.002729 / 0.004328 (-0.001599) | 0.048995 / 0.004250 (0.044744) | 0.044940 / 0.037052 (0.007888) | 0.288397 / 0.258489 (0.029908) | 0.317716 / 0.293841 (0.023875) | 0.029705 / 0.128546 (-0.098841) | 0.010972 / 0.075646 (-0.064674) | 0.058592 / 0.419271 (-0.360680) | 0.054640 / 0.043533 (0.011108) | 0.276456 / 0.255139 (0.021317) | 0.295119 / 0.283200 (0.011919) | 0.020032 / 0.141683 (-0.121651) | 1.175740 / 1.452155 (-0.276415) | 1.227246 / 1.492716 (-0.265471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092204 / 0.018006 (0.074197) | 0.300344 / 0.000490 (0.299855) | 0.000213 / 0.000200 (0.000013) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021540 / 0.037411 (-0.015871) | 0.076252 / 0.014526 (0.061726) | 0.087582 / 0.176557 (-0.088975) | 0.125977 / 0.737135 (-0.611159) | 0.090649 / 0.296338 (-0.205689) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294544 / 0.215209 (0.079335) | 2.883736 / 2.077655 (0.806082) | 1.570932 / 1.504120 (0.066812) | 1.449082 / 1.541195 (-0.092113) | 1.463262 / 1.468490 (-0.005228) | 0.559625 / 4.584777 (-4.025152) | 2.448593 / 3.745712 (-1.297119) | 2.663857 / 5.269862 (-2.606005) | 1.757812 / 4.565676 (-2.807865) | 0.061999 / 0.424275 (-0.362276) | 0.005100 / 0.007607 (-0.002507) | 0.343620 / 0.226044 (0.117575) | 3.487059 / 2.268929 (1.218130) | 1.963078 / 55.444624 (-53.481546) | 1.661758 / 6.876477 (-5.214719) | 1.799130 / 2.142072 (-0.342942) | 0.650194 / 4.805227 (-4.155034) | 0.117375 / 6.500664 (-6.383289) | 0.040957 / 0.075469 (-0.034512) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.037882 / 1.841788 (-0.803906) | 12.239784 / 8.074308 (4.165476) | 10.478186 / 10.191392 (0.286794) | 0.164446 / 0.680424 (-0.515978) | 0.014901 / 0.534201 (-0.519300) | 0.302485 / 0.579283 (-0.276798) | 0.283994 / 0.434364 (-0.150370) | 0.338473 / 0.540337 (-0.201864) | 0.468901 / 1.386936 (-0.918035) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fa934e275d240d9b1228b2f598bc96390299339 \"CML watermark\")\n"
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https://api.github.com/repos/huggingface/datasets/issues/6741 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6741/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6741/comments | https://api.github.com/repos/huggingface/datasets/issues/6741/events | https://github.com/huggingface/datasets/pull/6741 | 2,194,626,108 | PR_kwDODunzps5qEiu3 | 6,741 | Fix offline mode with single config | {
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} | [] | closed | false | null | [] | null | 2 | "2024-03-19T10:48:32" | "2024-03-25T16:35:21" | "2024-03-25T16:23:59" | MEMBER | null | Reported in https://github.com/huggingface/datasets/issues/4760
The cache was not able to reload a dataset with a single config form the cache if the config name is not specificed
For example
```python
from datasets import load_dataset, config
config.HF_DATASETS_OFFLINE = True
load_dataset("openai_humaneval")
```
This was due to a regression in https://github.com/huggingface/datasets/pull/6632 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6741). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005093 / 0.011353 (-0.006260) | 0.003317 / 0.011008 (-0.007692) | 0.064795 / 0.038508 (0.026287) | 0.030373 / 0.023109 (0.007263) | 0.258776 / 0.275898 (-0.017122) | 0.269768 / 0.323480 (-0.053711) | 0.004186 / 0.007986 (-0.003799) | 0.002630 / 0.004328 (-0.001699) | 0.048643 / 0.004250 (0.044392) | 0.044220 / 0.037052 (0.007168) | 0.265113 / 0.258489 (0.006624) | 0.292202 / 0.293841 (-0.001639) | 0.027468 / 0.128546 (-0.101079) | 0.010123 / 0.075646 (-0.065523) | 0.226869 / 0.419271 (-0.192402) | 0.035739 / 0.043533 (-0.007794) | 0.253193 / 0.255139 (-0.001946) | 0.271002 / 0.283200 (-0.012198) | 0.017201 / 0.141683 (-0.124482) | 1.105836 / 1.452155 (-0.346318) | 1.161559 / 1.492716 (-0.331158) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090481 / 0.018006 (0.072475) | 0.299013 / 0.000490 (0.298524) | 0.000220 / 0.000200 (0.000020) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017684 / 0.037411 (-0.019727) | 0.061580 / 0.014526 (0.047054) | 0.074370 / 0.176557 (-0.102186) | 0.119468 / 0.737135 (-0.617667) | 0.074671 / 0.296338 (-0.221668) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284778 / 0.215209 (0.069569) | 2.780241 / 2.077655 (0.702586) | 1.504025 / 1.504120 (-0.000095) | 1.386644 / 1.541195 (-0.154550) | 1.402038 / 1.468490 (-0.066452) | 0.555180 / 4.584777 (-4.029597) | 2.410973 / 3.745712 (-1.334740) | 2.773252 / 5.269862 (-2.496610) | 1.722784 / 4.565676 (-2.842892) | 0.062773 / 0.424275 (-0.361502) | 0.004959 / 0.007607 (-0.002648) | 0.337163 / 0.226044 (0.111119) | 3.356947 / 2.268929 (1.088019) | 1.880953 / 55.444624 (-53.563671) | 1.556049 / 6.876477 (-5.320427) | 1.578589 / 2.142072 (-0.563483) | 0.641993 / 4.805227 (-4.163234) | 0.118624 / 6.500664 (-6.382040) | 0.042202 / 0.075469 (-0.033268) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.995321 / 1.841788 (-0.846467) | 12.257597 / 8.074308 (4.183289) | 9.646214 / 10.191392 (-0.545178) | 0.131124 / 0.680424 (-0.549300) | 0.014119 / 0.534201 (-0.520082) | 0.287597 / 0.579283 (-0.291686) | 0.266983 / 0.434364 (-0.167381) | 0.328165 / 0.540337 (-0.212173) | 0.422405 / 1.386936 (-0.964531) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005091 / 0.011353 (-0.006262) | 0.003358 / 0.011008 (-0.007650) | 0.049136 / 0.038508 (0.010628) | 0.031075 / 0.023109 (0.007966) | 0.275047 / 0.275898 (-0.000851) | 0.296845 / 0.323480 (-0.026635) | 0.004949 / 0.007986 (-0.003037) | 0.002586 / 0.004328 (-0.001743) | 0.048164 / 0.004250 (0.043913) | 0.040754 / 0.037052 (0.003702) | 0.288715 / 0.258489 (0.030226) | 0.312383 / 0.293841 (0.018542) | 0.029372 / 0.128546 (-0.099174) | 0.010097 / 0.075646 (-0.065549) | 0.056752 / 0.419271 (-0.362520) | 0.033128 / 0.043533 (-0.010405) | 0.274986 / 0.255139 (0.019847) | 0.292692 / 0.283200 (0.009493) | 0.018309 / 0.141683 (-0.123374) | 1.190320 / 1.452155 (-0.261834) | 1.222529 / 1.492716 (-0.270188) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091717 / 0.018006 (0.073711) | 0.300278 / 0.000490 (0.299788) | 0.000217 / 0.000200 (0.000017) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021394 / 0.037411 (-0.016018) | 0.074918 / 0.014526 (0.060392) | 0.087461 / 0.176557 (-0.089095) | 0.125499 / 0.737135 (-0.611636) | 0.087484 / 0.296338 (-0.208854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296557 / 0.215209 (0.081348) | 2.905527 / 2.077655 (0.827872) | 1.624640 / 1.504120 (0.120520) | 1.505495 / 1.541195 (-0.035700) | 1.514066 / 1.468490 (0.045576) | 0.569376 / 4.584777 (-4.015401) | 2.448575 / 3.745712 (-1.297137) | 2.772805 / 5.269862 (-2.497057) | 1.757287 / 4.565676 (-2.808390) | 0.064209 / 0.424275 (-0.360066) | 0.005688 / 0.007607 (-0.001919) | 0.353175 / 0.226044 (0.127131) | 3.481591 / 2.268929 (1.212662) | 1.995384 / 55.444624 (-53.449240) | 1.684623 / 6.876477 (-5.191854) | 1.675750 / 2.142072 (-0.466323) | 0.644463 / 4.805227 (-4.160764) | 0.115393 / 6.500664 (-6.385271) | 0.040671 / 0.075469 (-0.034799) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.037487 / 1.841788 (-0.804301) | 11.902194 / 8.074308 (3.827886) | 10.148579 / 10.191392 (-0.042813) | 0.150261 / 0.680424 (-0.530163) | 0.015001 / 0.534201 (-0.519200) | 0.291008 / 0.579283 (-0.288275) | 0.278758 / 0.434364 (-0.155606) | 0.334037 / 0.540337 (-0.206301) | 0.419942 / 1.386936 (-0.966994) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dcd01046388fc052d37acc5a450bea69e3c57afc \"CML watermark\")\n"
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https://api.github.com/repos/huggingface/datasets/issues/6740 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6740/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6740/comments | https://api.github.com/repos/huggingface/datasets/issues/6740/events | https://github.com/huggingface/datasets/issues/6740 | 2,193,172,074 | I_kwDODunzps6CuSZq | 6,740 | Support for loading geotiff files as a part of the ImageFolder | {
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] | open | false | null | [] | null | 0 | "2024-03-18T20:00:39" | "2024-03-18T20:00:39" | null | NONE | null | ### Feature request
Request for adding rasterio support to load geotiff as a part of ImageFolder, instead of using PIL
### Motivation
As of now, there are many datasets in HuggingFace Hub which are predominantly focussed towards RemoteSensing or are from RemoteSensing. The current ImageFolder (if I have understood correctly) uses PIL. This is not really optimized because mostly these datasets have images with many channels and additional metadata. Using PIL makes one loose it unless we provide a custom script. Hence, maybe an API could be added to have this in common?
### Your contribution
If the issue is accepted - i can contribute the code, because I would like to have it automated and generalised. | {
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https://api.github.com/repos/huggingface/datasets/issues/6739 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6739/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6739/comments | https://api.github.com/repos/huggingface/datasets/issues/6739/events | https://github.com/huggingface/datasets/pull/6739 | 2,192,730,134 | PR_kwDODunzps5p-Bwe | 6,739 | Transpose images with EXIF Orientation tag | {
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} | [] | closed | false | null | [] | null | 2 | "2024-03-18T16:43:06" | "2024-03-19T15:35:57" | "2024-03-19T15:29:42" | CONTRIBUTOR | null | Closes https://github.com/huggingface/datasets/issues/6252 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6739). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005295 / 0.011353 (-0.006058) | 0.003402 / 0.011008 (-0.007606) | 0.062860 / 0.038508 (0.024352) | 0.029627 / 0.023109 (0.006518) | 0.238359 / 0.275898 (-0.037539) | 0.262940 / 0.323480 (-0.060540) | 0.003077 / 0.007986 (-0.004909) | 0.002676 / 0.004328 (-0.001652) | 0.048731 / 0.004250 (0.044480) | 0.043989 / 0.037052 (0.006936) | 0.255702 / 0.258489 (-0.002787) | 0.282667 / 0.293841 (-0.011174) | 0.028019 / 0.128546 (-0.100527) | 0.010195 / 0.075646 (-0.065451) | 0.205472 / 0.419271 (-0.213800) | 0.036551 / 0.043533 (-0.006982) | 0.243282 / 0.255139 (-0.011857) | 0.261925 / 0.283200 (-0.021274) | 0.020506 / 0.141683 (-0.121177) | 1.137228 / 1.452155 (-0.314927) | 1.183935 / 1.492716 (-0.308782) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100290 / 0.018006 (0.082284) | 0.316279 / 0.000490 (0.315790) | 0.000239 / 0.000200 (0.000039) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017979 / 0.037411 (-0.019432) | 0.061616 / 0.014526 (0.047090) | 0.072989 / 0.176557 (-0.103568) | 0.118667 / 0.737135 (-0.618468) | 0.074266 / 0.296338 (-0.222072) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287971 / 0.215209 (0.072762) | 2.845235 / 2.077655 (0.767581) | 1.501983 / 1.504120 (-0.002137) | 1.389824 / 1.541195 (-0.151370) | 1.415616 / 1.468490 (-0.052874) | 0.568727 / 4.584777 (-4.016050) | 2.368330 / 3.745712 (-1.377382) | 2.844329 / 5.269862 (-2.425532) | 1.809038 / 4.565676 (-2.756639) | 0.063699 / 0.424275 (-0.360576) | 0.004972 / 0.007607 (-0.002635) | 0.340092 / 0.226044 (0.114048) | 3.369146 / 2.268929 (1.100217) | 1.863423 / 55.444624 (-53.581201) | 1.608334 / 6.876477 (-5.268142) | 1.624479 / 2.142072 (-0.517594) | 0.632439 / 4.805227 (-4.172788) | 0.116862 / 6.500664 (-6.383802) | 0.042558 / 0.075469 (-0.032911) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967922 / 1.841788 (-0.873866) | 11.730612 / 8.074308 (3.656304) | 9.321333 / 10.191392 (-0.870059) | 0.142604 / 0.680424 (-0.537819) | 0.013934 / 0.534201 (-0.520267) | 0.285992 / 0.579283 (-0.293292) | 0.267639 / 0.434364 (-0.166724) | 0.324972 / 0.540337 (-0.215365) | 0.427077 / 1.386936 (-0.959859) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005806 / 0.011353 (-0.005547) | 0.003771 / 0.011008 (-0.007237) | 0.049542 / 0.038508 (0.011034) | 0.030182 / 0.023109 (0.007073) | 0.303923 / 0.275898 (0.028025) | 0.325623 / 0.323480 (0.002143) | 0.004327 / 0.007986 (-0.003659) | 0.002818 / 0.004328 (-0.001510) | 0.048237 / 0.004250 (0.043987) | 0.047490 / 0.037052 (0.010437) | 0.316556 / 0.258489 (0.058067) | 0.348352 / 0.293841 (0.054512) | 0.029444 / 0.128546 (-0.099102) | 0.010544 / 0.075646 (-0.065102) | 0.057382 / 0.419271 (-0.361890) | 0.056210 / 0.043533 (0.012677) | 0.305495 / 0.255139 (0.050356) | 0.321570 / 0.283200 (0.038370) | 0.019546 / 0.141683 (-0.122137) | 1.141732 / 1.452155 (-0.310423) | 1.223626 / 1.492716 (-0.269091) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093864 / 0.018006 (0.075858) | 0.309715 / 0.000490 (0.309226) | 0.000217 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022047 / 0.037411 (-0.015364) | 0.074885 / 0.014526 (0.060359) | 0.088440 / 0.176557 (-0.088117) | 0.127033 / 0.737135 (-0.610103) | 0.089048 / 0.296338 (-0.207290) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292624 / 0.215209 (0.077415) | 2.877592 / 2.077655 (0.799937) | 1.607036 / 1.504120 (0.102916) | 1.487819 / 1.541195 (-0.053376) | 1.517318 / 1.468490 (0.048828) | 0.553321 / 4.584777 (-4.031456) | 2.415577 / 3.745712 (-1.330135) | 2.691411 / 5.269862 (-2.578450) | 1.743395 / 4.565676 (-2.822282) | 0.062187 / 0.424275 (-0.362088) | 0.005073 / 0.007607 (-0.002534) | 0.342907 / 0.226044 (0.116863) | 3.402054 / 2.268929 (1.133126) | 1.979481 / 55.444624 (-53.465143) | 1.702885 / 6.876477 (-5.173592) | 1.868279 / 2.142072 (-0.273794) | 0.640095 / 4.805227 (-4.165132) | 0.117138 / 6.500664 (-6.383526) | 0.042197 / 0.075469 (-0.033272) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007495 / 1.841788 (-0.834292) | 12.037309 / 8.074308 (3.963001) | 10.227670 / 10.191392 (0.036278) | 0.149533 / 0.680424 (-0.530891) | 0.015282 / 0.534201 (-0.518919) | 0.287357 / 0.579283 (-0.291926) | 0.285109 / 0.434364 (-0.149255) | 0.324027 / 0.540337 (-0.216311) | 0.442482 / 1.386936 (-0.944454) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#19b40860acf3b3ba8db727fcf3b1b99ebb8d7e33 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6738/comments | https://api.github.com/repos/huggingface/datasets/issues/6738/events | https://github.com/huggingface/datasets/issues/6738 | 2,192,386,536 | I_kwDODunzps6CrSno | 6,738 | Dict feature is non-nullable while nested dict feature is | {
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] | closed | false | null | [] | null | 3 | "2024-03-18T14:31:47" | "2024-03-20T10:24:15" | "2024-03-19T20:05:20" | CONTRIBUTOR | null | When i try to create a `Dataset` object with None values inside a dict column, like this:
```python
from datasets import Dataset, Features, Value
Dataset.from_dict(
{
"dict": [{"a": 0, "b": 0}, None],
}, features=Features(
{"dict": {"a": Value("int16"), "b": Value("int16")}}
)
)
```
i get `ValueError: Got None but expected a dictionary instead`.
At the same time, having None in _nested_ dict feature works, for example, this doesn't throw any errors:
```python
from datasets import Dataset, Features, Value, Sequence
dataset = Dataset.from_dict(
{
"list_dict": [[{"a": 0, "b": 0}], None],
"sequence_dict": [[{"a": 0, "b": 0}], None],
}, features=Features({
"list_dict": [{"a": Value("int16"), "b": Value("int16")}],
"sequence_dict": Sequence({"a": Value("int16"), "b": Value("int16")}),
})
)
```
Other types of features also seem to be nullable (but I haven't checked all of them).
Version of `datasets` is the latest atm (2.18.0)
Is this an expected behavior or a bug? | {
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"It looks like a bug, by default every feature should be nullable.",
"I've linked a PR with a fix :)",
"@mariosasko awesome thank you!"
] |
https://api.github.com/repos/huggingface/datasets/issues/6737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6737/comments | https://api.github.com/repos/huggingface/datasets/issues/6737/events | https://github.com/huggingface/datasets/issues/6737 | 2,190,198,425 | I_kwDODunzps6Ci8aZ | 6,737 | Invalid pattern: '**' can only be an entire path component | {
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} | [] | open | false | null | [] | null | 1 | "2024-03-16T19:28:46" | "2024-03-18T11:49:26" | null | NONE | null | ### Describe the bug
ValueError: Invalid pattern: '**' can only be an entire path component
when loading any dataset
### Steps to reproduce the bug
import datasets
ds = datasets.load_dataset("TokenBender/code_instructions_122k_alpaca_style")
### Expected behavior
loading the dataset successfully
### Environment info
- `datasets` version: 2.18.0
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.11.7
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.12.2 | {
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"I couldn't reproduce the issue on my side on MacOS, I guess the issue comes from the recent `fsspec` on Windows.\r\n\r\nCan you try downgrading to `fsspec==2023.9.2` for now ? It would also be great to investigate this and see if we need a fix in `datasets` or `fsspec`"
] |
https://api.github.com/repos/huggingface/datasets/issues/6736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6736/comments | https://api.github.com/repos/huggingface/datasets/issues/6736/events | https://github.com/huggingface/datasets/issues/6736 | 2,190,181,422 | I_kwDODunzps6Ci4Qu | 6,736 | Mosaic Streaming (MDS) Support | {
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] | open | false | null | [] | null | 1 | "2024-03-16T18:42:04" | "2024-03-18T15:13:34" | null | NONE | null | ### Feature request
I'm a huge fan of the current HF Datasets `webdataset` integration (especially the built-in streaming support). However, I'd love to upload some robotics and multimodal datasets I've processed for use with [Mosaic Streaming](https://docs.mosaicml.com/projects/streaming/en/stable/), specifically their [MDS Format](https://docs.mosaicml.com/projects/streaming/en/stable/fundamentals/dataset_format.html#mds).
Because the shard files have similar semantics to WebDataset, I'm hoping that adding such support won't be too much trouble?
### Motivation
One of the downsides with WebDataset is a lack of out-of-the-box determinism (especially for large-scale training and reproducibility), easy job resumption, and the ability to quickly debug / visualize individual examples.
Mosaic Streaming provides a [great interface for this out of the box](https://docs.mosaicml.com/projects/streaming/en/stable/#key-features), so I'd love to see it supported in HF Datasets.
### Your contribution
Happy to help test things / provide example data. Can potentially submit a PR if maintainers could point me to the necessary WebDataset logic / steps for adding a new streaming format! | {
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"Hi ! that would be great :) Though note that `datasets` doesn't implement format-specific resuming when streaming, so in general I think it's better if users can use the mosaic-streaming library to read their MDS datasets. I wonder if they support `hf://` paths though...\r\n\r\nAnyway for those interested, the code for WebDataset is a single file here: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py.\r\n\r\nIt implements `_split_generators` that downloads files and returns the lists of splits (train/validation/test) and `_split_generators` to generate examples (dicts) from the downloaded files. Streaming is automatically supported by making download steps lazy and by extending `open()` to work with remote URLs."
] |
https://api.github.com/repos/huggingface/datasets/issues/6735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6735/comments | https://api.github.com/repos/huggingface/datasets/issues/6735/events | https://github.com/huggingface/datasets/pull/6735 | 2,189,132,932 | PR_kwDODunzps5px84g | 6,735 | Add `mode` parameter to `Image` feature | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6735). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005009 / 0.011353 (-0.006344) | 0.003547 / 0.011008 (-0.007461) | 0.063014 / 0.038508 (0.024506) | 0.027699 / 0.023109 (0.004589) | 0.247140 / 0.275898 (-0.028758) | 0.273610 / 0.323480 (-0.049870) | 0.003115 / 0.007986 (-0.004871) | 0.002712 / 0.004328 (-0.001616) | 0.049134 / 0.004250 (0.044883) | 0.041582 / 0.037052 (0.004530) | 0.269992 / 0.258489 (0.011503) | 0.294516 / 0.293841 (0.000675) | 0.027818 / 0.128546 (-0.100728) | 0.010568 / 0.075646 (-0.065078) | 0.207710 / 0.419271 (-0.211561) | 0.035767 / 0.043533 (-0.007766) | 0.260058 / 0.255139 (0.004919) | 0.277615 / 0.283200 (-0.005585) | 0.020192 / 0.141683 (-0.121491) | 1.116863 / 1.452155 (-0.335292) | 1.156868 / 1.492716 (-0.335848) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095087 / 0.018006 (0.077081) | 0.303249 / 0.000490 (0.302759) | 0.000215 / 0.000200 (0.000015) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018866 / 0.037411 (-0.018545) | 0.063853 / 0.014526 (0.049328) | 0.073863 / 0.176557 (-0.102693) | 0.121399 / 0.737135 (-0.615737) | 0.076014 / 0.296338 (-0.220325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289843 / 0.215209 (0.074634) | 2.844085 / 2.077655 (0.766431) | 1.528022 / 1.504120 (0.023902) | 1.397352 / 1.541195 (-0.143843) | 1.394676 / 1.468490 (-0.073814) | 0.555899 / 4.584777 (-4.028878) | 2.354010 / 3.745712 (-1.391702) | 2.737715 / 5.269862 (-2.532146) | 1.731260 / 4.565676 (-2.834416) | 0.062315 / 0.424275 (-0.361960) | 0.004920 / 0.007607 (-0.002687) | 0.342921 / 0.226044 (0.116877) | 3.416529 / 2.268929 (1.147600) | 1.862941 / 55.444624 (-53.581684) | 1.599661 / 6.876477 (-5.276816) | 1.617200 / 2.142072 (-0.524873) | 0.635129 / 4.805227 (-4.170099) | 0.121651 / 6.500664 (-6.379013) | 0.041867 / 0.075469 (-0.033602) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990825 / 1.841788 (-0.850962) | 11.435576 / 8.074308 (3.361268) | 9.490194 / 10.191392 (-0.701198) | 0.133295 / 0.680424 (-0.547129) | 0.014061 / 0.534201 (-0.520140) | 0.288648 / 0.579283 (-0.290635) | 0.268874 / 0.434364 (-0.165490) | 0.323288 / 0.540337 (-0.217049) | 0.426090 / 1.386936 (-0.960846) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006712 / 0.011353 (-0.004641) | 0.003723 / 0.011008 (-0.007285) | 0.049814 / 0.038508 (0.011306) | 0.039323 / 0.023109 (0.016213) | 0.279244 / 0.275898 (0.003346) | 0.297139 / 0.323480 (-0.026341) | 0.004197 / 0.007986 (-0.003788) | 0.002753 / 0.004328 (-0.001576) | 0.048820 / 0.004250 (0.044569) | 0.049593 / 0.037052 (0.012541) | 0.287247 / 0.258489 (0.028758) | 0.338078 / 0.293841 (0.044237) | 0.029303 / 0.128546 (-0.099243) | 0.010292 / 0.075646 (-0.065354) | 0.057852 / 0.419271 (-0.361419) | 0.053390 / 0.043533 (0.009857) | 0.275155 / 0.255139 (0.020016) | 0.292891 / 0.283200 (0.009692) | 0.020007 / 0.141683 (-0.121676) | 1.161731 / 1.452155 (-0.290424) | 1.232162 / 1.492716 (-0.260555) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092848 / 0.018006 (0.074842) | 0.301180 / 0.000490 (0.300690) | 0.000236 / 0.000200 (0.000036) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022477 / 0.037411 (-0.014934) | 0.077012 / 0.014526 (0.062486) | 0.087335 / 0.176557 (-0.089222) | 0.126761 / 0.737135 (-0.610374) | 0.089249 / 0.296338 (-0.207090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290722 / 0.215209 (0.075513) | 2.884485 / 2.077655 (0.806830) | 1.565775 / 1.504120 (0.061656) | 1.442369 / 1.541195 (-0.098825) | 1.453995 / 1.468490 (-0.014495) | 0.563193 / 4.584777 (-4.021584) | 2.413610 / 3.745712 (-1.332102) | 2.684567 / 5.269862 (-2.585295) | 1.753322 / 4.565676 (-2.812354) | 0.061879 / 0.424275 (-0.362396) | 0.005080 / 0.007607 (-0.002527) | 0.347274 / 0.226044 (0.121229) | 3.435836 / 2.268929 (1.166907) | 1.937893 / 55.444624 (-53.506731) | 1.657824 / 6.876477 (-5.218653) | 1.777767 / 2.142072 (-0.364305) | 0.656757 / 4.805227 (-4.148471) | 0.117144 / 6.500664 (-6.383520) | 0.040691 / 0.075469 (-0.034778) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012435 / 1.841788 (-0.829353) | 12.038001 / 8.074308 (3.963693) | 10.363947 / 10.191392 (0.172555) | 0.140711 / 0.680424 (-0.539713) | 0.014937 / 0.534201 (-0.519264) | 0.291070 / 0.579283 (-0.288213) | 0.277180 / 0.434364 (-0.157184) | 0.327433 / 0.540337 (-0.212904) | 0.439767 / 1.386936 (-0.947169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b55ec53e980855d71ae22f8b3d12b2a0d476a51 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6734/comments | https://api.github.com/repos/huggingface/datasets/issues/6734/events | https://github.com/huggingface/datasets/issues/6734 | 2,187,646,694 | I_kwDODunzps6CZNbm | 6,734 | Tokenization slows towards end of dataset | {
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} | [] | open | false | null | [] | null | 2 | "2024-03-15T03:27:36" | "2024-03-15T15:27:59" | null | NONE | null | ### Describe the bug
Mapped tokenization slows down substantially towards end of dataset.
train set started off very slow, caught up to 20k then tapered off til the end.
what's particularly strange is that the tokenization crashed a few times before due to errors with invalid tokens somewhere or corrupted downloads, and the speed ups/downs consistently happened the same times
```bash
Running tokenizer on dataset (num_proc=48): 0%| | 847000/881416735 [12:18<252:45:45, 967.72 examples/s]
Running tokenizer on dataset (num_proc=48): 0%| | 848000/881416735 [12:19<224:16:10, 1090.66 examples/s]
Running tokenizer on dataset (num_proc=48): 10%|▉ | 84964000/881416735 [3:48:00<11:21:34, 19476.01 examples/s]
Running tokenizer on dataset (num_proc=48): 10%|▉ | 84967000/881416735 [3:48:00<12:04:01, 18333.79 examples/s]
Running tokenizer on dataset (num_proc=48): 61%|██████ | 538631977/881416735 [13:46:40<27:50:04, 3420.84 examples/s]
Running tokenizer on dataset (num_proc=48): 61%|██████ | 538632977/881416735 [13:46:40<23:48:20, 3999.77 examples/s]
Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881365886/881416735 [38:30:19<04:34, 185.10 examples/s]
Running tokenizer on dataset (num_proc=48): 100%|█████████▉| 881366886/881416735 [38:30:25<04:36, 180.57 examples/s]
```
and validation set as well
```bash
Running tokenizer on dataset (num_proc=48): 90%|████████▉ | 41544000/46390354 [28:44<02:37, 30798.76 examples/s]
Running tokenizer on dataset (num_proc=48): 90%|████████▉ | 41550000/46390354 [28:44<02:08, 37698.08 examples/s]
Running tokenizer on dataset (num_proc=48): 96%|█████████▋| 44747422/46390354 [2:15:48<12:22:44, 36.87 examples/s]
Running tokenizer on dataset (num_proc=48): 96%|█████████▋| 44747422/46390354 [2:16:00<12:22:44, 36.87 examples/s]
```
### Steps to reproduce the bug
using the following kwargs
```python
with accelerator.main_process_first():
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=48
load_from_cache_file=True,
desc=f"Grouping texts in chunks of {block_size}",
)
```
running through slurm script
```bash
#SBATCH --partition=gpu-nvidia-a100
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --gpus-per-task=8
#SBATCH --cpus-per-task=96
```
using this dataset https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T
### Expected behavior
Constant speed throughout
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-5.15.0-1049-aws-x86_64-with-glibc2.10
- Python version: 3.8.18
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | {
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"Hi ! First note that if the dataset is not heterogeneous / shuffled, there might be places in the data with shorter texts that are faster to tokenize.\r\n\r\nMoreover, the way `num_proc` works is by slicing the dataset and passing each slice to a process to run the `map()` function. So at the very end of `map()`, some processes might have finished transforming their slice of data while others are still running, causing the throughput to become lower.",
"I did see some comments about how num_proc=None could help and outputting numpy arrays can also help in the docs, but this seems quite odd now dropping down to 1it/s\r\n\r\n```bash\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46048888/46390354 [12:33:30<4:20:32, 21.84 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46049888/46390354 [12:36:11<8:37:59, 10.95 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46050888/46390354 [12:46:35<24:56:56, 3.78 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46051888/46390354 [12:56:43<35:08:10, 2.68 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46052888/46390354 [13:06:58<42:05:41, 2.23 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46053888/46390354 [13:16:01<44:40:18, 2.09 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46054888/46390354 [13:25:11<46:35:28, 2.00 examples/s]\r\nRunning tokenizer on dataset (num_proc=48): 99%|█████████▉| 46055888/46390354 [13:34:23<47:55:34, 1.94 examples/s]\r\n```\r\n\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/6733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6733/comments | https://api.github.com/repos/huggingface/datasets/issues/6733/events | https://github.com/huggingface/datasets/issues/6733 | 2,186,811,724 | I_kwDODunzps6CWBlM | 6,733 | EmptyDatasetError when loading dataset downloaded with HuggingFace cli | {
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} | [] | open | false | null | [] | null | 1 | "2024-03-14T16:41:27" | "2024-03-15T18:09:02" | null | NONE | null | ### Describe the bug
I am using a cluster that does not have access to the internet when given a job. I tried downloading the dataset using the huggingface-cli command and then loading it with load_dataset but I get an error:
```raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None```
The dataset I'm using is "lmsys/chatbot_arena_conversations". The folder structure is
- README.md
- data
- train-00000-of-00001-cced8514c7ed782a.parquet
### Steps to reproduce the bug
1. Download dataset using HuggingFace CLI: ```huggingface-cli download lmsys/chatbot_arena_conversations --local-dir ./lmsys/chatbot_arena_conversations```
2. In Python
```
from datasets import load_dataset
load_dataset("lmsys/chatbot_arena_conversations")
```
### Expected behavior
Should return a Dataset Dict in the form of
```
DatasetDict({
train: Dataset({
features: [...],
num_rows: 33,000
})
})
```
### Environment info
Python 3.11.5
Datasets 2.18.0
Transformers 4.38.2
Pytorch 2.2.0
Pyarrow 15.0.1
Rocky Linux release 8.9 (Green Obsidian)
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"Hi! `datasets` is not compatible with `huggingface_hub`'s cache structure, hence the error.\r\n\r\nYou can track https://github.com/huggingface/datasets/issues/5080 to get notified when this is implemented."
] |
https://api.github.com/repos/huggingface/datasets/issues/6731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6731/comments | https://api.github.com/repos/huggingface/datasets/issues/6731/events | https://github.com/huggingface/datasets/issues/6731 | 2,182,844,673 | I_kwDODunzps6CG5EB | 6,731 | Unexpected behavior when using load_dataset with streaming=True in a for loop | {
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} | [] | open | false | null | [] | null | 1 | "2024-03-12T23:26:43" | "2024-03-14T15:27:02" | null | NONE | null | ### Describe the bug
### My Code
```
from datasets import load_dataset
res=[]
for i in [0,1]:
di=load_dataset(
"json",
data_files='path_to.json',
split='train',
streaming=True,
).map(lambda x: {"source": i})
res.append(di)
for e in res[0]:
print(e)
```
### Unexpected Behavior
Data in `res[0]` has `source=1`. However the expected value is 0.
### FYI
I further switch `streaming` to `False`. And the output value is as expected (0). So there may exist bugs in setting `streaming=True` in a for loop.
### Environment
Python 3.8.0
datasets==2.18.0
transformers==4.28.1
### Steps to reproduce the bug
1. Create a Json file with any content.
2. Run the provided code.
3. Switch `streaming` to `False` and run again to see the expected behavior.
### Expected behavior
The expected behavior is the data are mapped with its corresponding value in the for loop.
### Environment info
Python 3.8.0
datasets==2.18.0
transformers==4.28.1
Ubuntu 20.04 | {
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"This is normal behavior in python when using `lambda`: the `i` defined in your `lambda` refers to the global variable `i` in your loop, and `i` equals to `1` when you run your `for e in res[0]` line.\r\n\r\nYou should pass `fn_kwargs` that will be passed to your `lambda` instead of using the global variable:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nres=[]\r\nfor i in [0,1]:\r\n di = load_dataset(\r\n \"json\", \r\n data_files='path_to.json', \r\n split='train',\r\n streaming=True, \r\n ).map(lambda x, source: {\"source\": source}, fn_kwargs={\"source\": i})\r\n\r\n res.append(di)\r\n\r\nfor e in res[0]:\r\n print(e)\r\n```\r\n\r\nThis doesn't happen in non-streaming since in that case `map` is executed while the variable `i` has the right value. In streaming mode, `map` is executed on-the-fly when you iterate on the dataset."
] |
https://api.github.com/repos/huggingface/datasets/issues/6730 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6730/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6730/comments | https://api.github.com/repos/huggingface/datasets/issues/6730/events | https://github.com/huggingface/datasets/pull/6730 | 2,181,881,499 | PR_kwDODunzps5pZDsB | 6,730 | Deprecate Pandas builder | {
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} | [] | closed | false | null | [] | null | 2 | "2024-03-12T15:12:13" | "2024-03-12T17:42:33" | "2024-03-12T17:36:24" | CONTRIBUTOR | null | The Pandas packaged builder is undocumented and relies on `pickle` to read the data, making it **unsafe**. Moreover, I haven't seen a single instance of this builder being used (not even using the GH/Hub search), so we should deprecate it. | {
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} | true | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6730). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005301 / 0.011353 (-0.006052) | 0.003701 / 0.011008 (-0.007307) | 0.065830 / 0.038508 (0.027322) | 0.029791 / 0.023109 (0.006682) | 0.251676 / 0.275898 (-0.024222) | 0.283824 / 0.323480 (-0.039655) | 0.003083 / 0.007986 (-0.004903) | 0.004144 / 0.004328 (-0.000185) | 0.053670 / 0.004250 (0.049419) | 0.042020 / 0.037052 (0.004968) | 0.266389 / 0.258489 (0.007899) | 0.296740 / 0.293841 (0.002900) | 0.028320 / 0.128546 (-0.100226) | 0.010604 / 0.075646 (-0.065042) | 0.219881 / 0.419271 (-0.199390) | 0.036216 / 0.043533 (-0.007317) | 0.255718 / 0.255139 (0.000579) | 0.275808 / 0.283200 (-0.007392) | 0.018407 / 0.141683 (-0.123276) | 1.140007 / 1.452155 (-0.312148) | 1.174005 / 1.492716 (-0.318711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091230 / 0.018006 (0.073224) | 0.300704 / 0.000490 (0.300215) | 0.000207 / 0.000200 (0.000007) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018950 / 0.037411 (-0.018461) | 0.062177 / 0.014526 (0.047651) | 0.073968 / 0.176557 (-0.102589) | 0.122161 / 0.737135 (-0.614974) | 0.075001 / 0.296338 (-0.221338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285675 / 0.215209 (0.070466) | 2.794176 / 2.077655 (0.716522) | 1.478666 / 1.504120 (-0.025454) | 1.361843 / 1.541195 (-0.179351) | 1.383847 / 1.468490 (-0.084643) | 0.568610 / 4.584777 (-4.016167) | 2.402351 / 3.745712 (-1.343361) | 2.860772 / 5.269862 (-2.409089) | 1.768588 / 4.565676 (-2.797089) | 0.063257 / 0.424275 (-0.361018) | 0.004998 / 0.007607 (-0.002609) | 0.340897 / 0.226044 (0.114853) | 3.340238 / 2.268929 (1.071310) | 1.836434 / 55.444624 (-53.608190) | 1.556844 / 6.876477 (-5.319633) | 1.610685 / 2.142072 (-0.531388) | 0.644941 / 4.805227 (-4.160286) | 0.117593 / 6.500664 (-6.383072) | 0.042803 / 0.075469 (-0.032666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.979181 / 1.841788 (-0.862607) | 11.901365 / 8.074308 (3.827057) | 9.587943 / 10.191392 (-0.603449) | 0.139648 / 0.680424 (-0.540776) | 0.013904 / 0.534201 (-0.520297) | 0.291249 / 0.579283 (-0.288034) | 0.260737 / 0.434364 (-0.173627) | 0.326000 / 0.540337 (-0.214338) | 0.433459 / 1.386936 (-0.953477) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005503 / 0.011353 (-0.005850) | 0.003738 / 0.011008 (-0.007270) | 0.049137 / 0.038508 (0.010629) | 0.031484 / 0.023109 (0.008374) | 0.265783 / 0.275898 (-0.010115) | 0.295125 / 0.323480 (-0.028354) | 0.004074 / 0.007986 (-0.003911) | 0.002707 / 0.004328 (-0.001622) | 0.048340 / 0.004250 (0.044089) | 0.045453 / 0.037052 (0.008401) | 0.276500 / 0.258489 (0.018011) | 0.312002 / 0.293841 (0.018162) | 0.029139 / 0.128546 (-0.099408) | 0.010445 / 0.075646 (-0.065201) | 0.057486 / 0.419271 (-0.361785) | 0.052386 / 0.043533 (0.008853) | 0.267099 / 0.255139 (0.011960) | 0.283193 / 0.283200 (-0.000007) | 0.018368 / 0.141683 (-0.123315) | 1.136207 / 1.452155 (-0.315948) | 1.178418 / 1.492716 (-0.314298) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089270 / 0.018006 (0.071264) | 0.301087 / 0.000490 (0.300598) | 0.000208 / 0.000200 (0.000008) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021991 / 0.037411 (-0.015421) | 0.075357 / 0.014526 (0.060831) | 0.087781 / 0.176557 (-0.088775) | 0.126923 / 0.737135 (-0.610212) | 0.088491 / 0.296338 (-0.207847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293653 / 0.215209 (0.078444) | 2.872156 / 2.077655 (0.794501) | 1.559229 / 1.504120 (0.055109) | 1.441201 / 1.541195 (-0.099993) | 1.472642 / 1.468490 (0.004152) | 0.588463 / 4.584777 (-3.996314) | 2.447685 / 3.745712 (-1.298028) | 2.755752 / 5.269862 (-2.514110) | 1.796591 / 4.565676 (-2.769086) | 0.068024 / 0.424275 (-0.356252) | 0.005148 / 0.007607 (-0.002459) | 0.343572 / 0.226044 (0.117528) | 3.347856 / 2.268929 (1.078927) | 1.945977 / 55.444624 (-53.498647) | 1.648953 / 6.876477 (-5.227524) | 1.804468 / 2.142072 (-0.337604) | 0.651034 / 4.805227 (-4.154193) | 0.118130 / 6.500664 (-6.382534) | 0.041019 / 0.075469 (-0.034450) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.020461 / 1.841788 (-0.821327) | 12.514237 / 8.074308 (4.439929) | 10.696276 / 10.191392 (0.504884) | 0.154549 / 0.680424 (-0.525874) | 0.015964 / 0.534201 (-0.518237) | 0.290392 / 0.579283 (-0.288891) | 0.276074 / 0.434364 (-0.158290) | 0.326253 / 0.540337 (-0.214085) | 0.440383 / 1.386936 (-0.946553) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29ffc270da34de70cf8e28b2ebeadba1c06d8730 \"CML watermark\")\n"
] |
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