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PR_kwDODunzps7BiJps
| 7,992
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Add `IterableDataset.reshard()`
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To increase the number of shards of a dataset, you can use [`IterableDataset.reshard`]:
```py
>>> dataset.reshard()
IterableDataset({
features: ['label', 'title', 'content'],
num_shards: 3600
})
```
The resharding mechanism depends on the dataset file format.
For example for Parquet, it reshards using row groups instead of having one file per shard.
We can implement other formats later (e.g. JSON Lines, CSV can be split by recovering line boundaries from arbitrary locations)
Other details:
* fixed concatenate after shuffling, now it correctly shuffles the shards: close https://github.com/huggingface/datasets/issues/7196
* fixed interleave after split_by_node: close https://github.com/huggingface/datasets/issues/7868
* changed a bit how the effective seed works in multi node/worker/epoch situations
related to https://github.com/huggingface/datasets/issues/7917
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I_kwDODunzps7oRb0h
| 7,991
|
list(api.list_datasets()) giving jsondecode error
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[] | 2026-02-04T14:39:46
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NONE
| null | null | null | null |
i am using the python api wrapper to list all datasets available on hugging face. This is for research, and i need all datasets to determine which % have language tags and other related questions requiring the total list. However, the following code that worked a few months ago:
```
from huggingface_hub import HfApi
api = HfApi(token=token)
datasets = list(api.list_datasets())
```
now gives a JSONDecodeError when reaching 2000 results. My understanding is that this is a pagination issue, as there is no cursor exposed to the API and so it doesnt know where to read from after the limit.
Is there any way to find all datasets available and combine them into a list?
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Dataset.map crashes when first examples return None and later examples return dict — writer not initialized
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[] | 2026-02-04T10:43:20
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| 2024-08-21T09:35:06
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NONE
| null | null | null | null |
### Describe the bug
I detected a serious [bug from datasets/arrow_dataset.py](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L3676)
---
**Description of the bug**
`Dataset.map` crashes with `writer is None` when the map function returns `None` for the first few examples and a dictionary (or `pa.Table` / DataFrame) for later examples. This happens because the internal writer is initialized only when `i == 0` (or `i[0] == 0` in batched mode), but `update_data` is determined lazily after processing the first example/batch.
**Steps to reproduce**
```python
from datasets import Dataset
ds = Dataset.from_dict({"x": [1, 2, 3]})
def fn(example, idx):
if idx < 2:
return None
return {"x": [example["x"] * 10]}
list(ds.map(fn, with_indices=True))
```
**Expected behavior**
* The function should work regardless of when `update_data` becomes `True`.
* Writer should be initialized the first time a non-`None` return occurs, not tied to the first index.
**Environment info**
* `datasets` version: <insert your version>
* Python version: 3.12
* OS: <insert your OS>
**Suggested fix**
Replace `if i == 0` / `if i[0] == 0` checks with `if writer is None` when initializing the writer.
---
### Steps to reproduce the bug
Here's a ready-to-use version you can paste into that section:
---
### Steps to reproduce the bug
```python
from datasets import Dataset
# Create a minimal dataset
ds = Dataset.from_dict({"x": [1, 2, 3]})
# Define a map function that returns None for first examples, dict later
def fn(example, idx):
if idx < 2:
return None
return {"x": [example["x"] * 10]}
# Apply map with indices
list(ds.map(fn, with_indices=True))
```
**Expected:** function executes without errors.
**Observed:** crashes with `AttributeError: 'NoneType' object has no attribute 'write'` because the internal writer is not initialized when the first non-None return happens after i > 0.
---
This is minimal and clearly demonstrates the exact failure condition (`None` early, `dict` later).
### Expected behavior
---
**Expected behavior**
The `Dataset.map` function should handle map functions that return `None` for some examples and a dictionary (or `pa.Table` / DataFrame) for later examples. In this case, the internal writer should be initialized when the first non-`None` value is returned, so that the dataset can be updated without crashing. The code should run successfully for all examples and return the updated dataset.
---
### Environment info
- python3.12
- datasets==3.6.0 [but the latest version still has this problem]
- transformers==4.55.2
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PR_kwDODunzps7BaxDx
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|
Remove pre-release workaround in CI for `transformers v5` and `huggingface_hub v1`
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] | 2026-02-04T09:42:49
| 2026-02-04T15:20:04
| 2026-02-04T15:20:02
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This PR removes workaround for pre-release `transformers v5.*` / `huggingface_hub v1.*` in `test_py314_future` job since they are now officially released.
cc @Wauplin just for viz since you introduced this in https://github.com/huggingface/datasets/pull/7783.
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`Dataset.map()` breaks when `function` calls `import polars as pl` and `num_proc`>1: "UnboundLocalError: cannot access local variable 'pl' where it is not associated with a value"
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| 2026-02-04T08:42:23
| 2024-08-21T09:35:06
|
NONE
| null | null | null | null |
### Describe the bug
# Repro
These two conditions seem to consistently reproduce the issue:
- function passed to `Dataset.map()` explicitly or implicitly calls `import polars as pl`
- `num_proc` > 1
# Trace
```
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "c:\Users\{redacted}\.venv\Lib\site-packages\multiprocess\pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "c:\Users\{redacted}\.venv\Lib\site-packages\datasets\utils\py_utils.py", line 586, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\Users\{redacted}\.venv\Lib\site-packages\datasets\arrow_dataset.py", line 3687, in _map_single
and isinstance(example, pl.DataFrame)
^^
UnboundLocalError: cannot access local variable 'pl' where it is not associated with a value
"""
The above exception was the direct cause of the following exception:
UnboundLocalError Traceback (most recent call last)
Cell In[2], [line 9](vscode-notebook-cell:?execution_count=2&line=9)
6 import polars as pl
7 return {'squared': sample['n'] ** 2}
----> [9](vscode-notebook-cell:?execution_count=2&line=9) ds.map(square, num_proc=2)
File c:\Users\{redacted}\.venv\Lib\site-packages\datasets\arrow_dataset.py:562, in transmit_format.<locals>.wrapper(*args, **kwargs)
555 self_format = {
556 "type": self._format_type,
557 "format_kwargs": self._format_kwargs,
558 "columns": self._format_columns,
559 "output_all_columns": self._output_all_columns,
560 }
561 # apply actual function
--> [562](file:///C:/Users/{redacted}/.venv/Lib/site-packages/datasets/arrow_dataset.py:562) out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
563 datasets: list["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
564 # re-apply format to the output
File c:\Users\{redacted}\.venv\Lib\site-packages\datasets\arrow_dataset.py:3334, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc, try_original_type)
3331 os.environ = prev_env
3332 logger.info(f"Spawning {num_proc} processes")
-> [3334](file:///C:/Users/{redacted}/.venv/Lib/site-packages/datasets/arrow_dataset.py:3334) for rank, done, content in iflatmap_unordered(
3335 pool, Dataset._map_single, kwargs_iterable=unprocessed_kwargs_per_job
3336 ):
3337 check_if_shard_done(rank, done, content)
3339 pool.close()
File c:\Users\{redacted}\.venv\Lib\site-packages\datasets\utils\py_utils.py:626, in iflatmap_unordered(pool, func, kwargs_iterable)
623 finally:
624 if not pool_changed:
625 # we get the result in case there's an error to raise
--> [626](file:///C:/Users/{redacted}/.venv/Lib/site-packages/datasets/utils/py_utils.py:626) [async_result.get(timeout=0.05) for async_result in async_results]
File c:\Users\{redacted}\.venv\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout)
772 return self._value
773 else:
--> [774](file:///C:/Users/{redacted}/.venv/Lib/site-packages/multiprocess/pool.py:774) raise self._value
UnboundLocalError: cannot access local variable 'pl' where it is not associated with a value
```
# Why `import polars` in a worker function?
To my knowledge `Dataset.map()` doesn't support a worker init function, and objects useful inside a function aren't always pickable, so I commonly use this pattern to essentially construct the unpickable object in a worker process:
```python
def func(example, **kwargs):
if 'my_unpickable_object' not in globals():
from my_module import MyClass
my_unpickable_object = MyClass(**kwargs)
return {'newcol': my_unpickable_object.calculate_something(example['n'])}
ds = Dataset.load_from_disk(...)
ds.map(func, num_proc=2, ...)
```
and here `from my_module import MyClass` may implicitly call `import polars as pl` e.g. when `my_module.py` has that line, or when it imports some other module containing `import polars as pl`.
# A workaround
Things seem to work ok if I don't do any import inside `func`, but instead pass a class already imported outside `func` as a contructor, like below. Although I'm unsure how many scenarios this workaround can cover.
```python
from my_module import MyClass
def func(example, constructor=MyClass, **kwargs):
if 'my_unpickable_object' not in globals():
my_unpickable_object = constructor(**kwargs)
return {'newcol': my_unpickable_object.calculate_something(example['n'])}
ds = Dataset.load_from_disk(...)
ds.map(func, num_proc=2, ...)
```
# My speculations
Before the crash point, on line 3656 of `arrow_dataset.py` it reads:
```python
if config.POLARS_AVAILABLE and "polars" in sys.modules:
import polars as pl
````
My guess is these conditions are somehow messed up in a worker process, and it ended up not entering the `if` block.
### Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({'n': list(range(10_000))})
def square(sample):
import polars as pl
return {'squared': sample['n'] ** 2}
ds.map(square, num_proc=2)
```
### Expected behavior
```
Dataset({
features: ['n', 'squared'],
num_rows: 10000
})
```
### Environment info
- `datasets` version: 4.5.0
- Platform: Windows-11-10.0.26200-SP0
- Python version: 3.12.10
- `huggingface_hub` version: 0.31.2
- PyArrow version: 21.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
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PR_kwDODunzps7BX0pY
| 7,987
|
Fix index out of bound error with original_shard_lengths.
|
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I have gotten the following error
```
original_shard_lengths[original_shard_id] += 1
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
```
Not sure what causes it, but this fixes the error. This may not be the proper fix for the root cause though.
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I_kwDODunzps7oBw7L
| 7,986
|
`Dataset.map()` causes cache miss/fingerprint change when closure captures self containing non-deterministic state.
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"I suggest metion this in docs specifically for attention with use, tell users to pass arguments with `fn_kwargs` param or using `functools.partial` to create a pure funcion."
] | 2026-02-03T19:16:49
| 2026-02-03T19:20:00
| 2024-08-21T09:35:06
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NONE
| null | null | null | null |
### Describe the bug
When using `.map()` with a function defined inside a **class (of which has any non-deterministic states)** method (a closure), if that function captures `self` to access a configuration variable (e.g., self.foo), the fingerprint mechanism serializes the entire class instance state.
If the class instance contains any non-deterministic state (such as random seeds, loggers, or distinct object IDs—in my case, PyTorch Lightning's `LightningDataModule`), the fingerprint changes on every run, rendering the cache useless.
While this may be intended behavior for `dill`, it is a significant "gotcha" for users migrating code into classes, as unrelated state changes cause massive re-processing overhead.
Real world "cache explosion" screenshot caused by the fingerprint mismatch:
<img width="942" height="382" alt="Image" src="https://github.com/user-attachments/assets/2fb0acba-ac07-4f00-bf30-c1ac932c9072" />
### Steps to reproduce the bug
Minimal reproduction code block:
```python3
import datasets
import uuid
# Prevent logging spam
datasets.logging.set_verbosity_error()
class ReproduceIssue:
def __init__(self):
# This is the variable we actually care about in the map function
self.foo = 32
# This simulates "dirty" internal state often found in framework classes
# (e.g., unique IDs, pointers to loggers, thread locks, or random seeds)
self.hidden_state = uuid.uuid4()
self.dataset = datasets.Dataset.from_dict({"strokes": [1, 2, 3]})
def setup(self):
# Closure captures 'self' to access 'self.foo'
def preprocess(batch):
# Accessing self binds the function to the specific instance state
_ = self.foo
return {"foo": batch["bar"]}
return self.dataset.map(preprocess, batched=True)
print("--- Run 1 ---")
inst1 = ReproduceIssue()
ds1 = inst1.setup()
print(f"Fingerprint 1: {ds1._fingerprint}")
print("\n--- Run 2 (New Instance) ---")
inst2 = ReproduceIssue()
ds2 = inst2.setup()
print(f"Fingerprint 2: {ds2._fingerprint}")
if ds1._fingerprint != ds2._fingerprint:
print("\n❌ ISSUE REPRODUCED: Fingerprints differ (Cache Miss).")
else:
print("\n✅ Fingerprints match.")
```
Result:
```
--- Run 1 ---
Mapping: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2025.26 examples/s]
Fingerprint 1: 1ce6104f9e97912a
--- Run 2 (New Instance) ---
Mapping: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2300.77 examples/s]
Fingerprint 2: c0fc011ff86ea571
--- Result ---
❌ CACHE MISS: Fingerprints are different!
```
### Expected behavior
The fingerprint should ideally depend **only on the bytecode of the function and the values of the variables actually accessed** (`self.foo`), rather than the state of the whole object self.
### Environment info
datasets version: 4.5.0, platform: any, python version: 3.13.
This was encountered while subclassing torch lightning's `LightningDataModule`. These objects inherently **contain internal state that differs per instance**.
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Remove unused data files optims
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7985). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-03T17:58:30
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this fixes module inference when there are many metadata files
e.g. the lance dataset at https://huggingface.co/datasets/davanstrien/encyclopaedia-britannica-lance has > 200 metadata files
those optims are not used anymore, they come from a time we were dealing with slow data files iterators instead of lists
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Data
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|
Add Zarr streaming support (POC)
|
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Add initial Zarr streaming support (POC).
This introduces a `zarr` packaged module and docs/tests to validate basic loading.
Note: I pushed a follow-up commit to fix an accidental duplication in `benchmarks/benchmark_zarr_streaming.py` (file now contains a single benchmark script).
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Fix unstable tokenizer fingerprinting (enables map cache reuse)
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Fix unstable dataset fingerprinting when hashing `PreTrainedTokenizerFast`.
Some tokenizers backed by `tokenizers.Tokenizer` mutate runtime settings (padding/truncation) when called, which can change the serialized state and make dataset fingerprints unstable. That prevents `.map(load_from_cache_file=True)` from reusing cache files.
Fix: when hashing, temporarily disable backend padding/truncation so runtime settings don’t affect the fingerprint, then restore the original settings.
Includes a regression test showing `Hasher.hash(tokenizer)` stays stable after calling the tokenizer.
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PR_kwDODunzps7A-V7J
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Support pandas 3
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PR_kwDODunzps7A9Wrj
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Drop python 3.9
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EOL was a few months ago, and transformers doesn't support 3.9 anymore
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PR_kwDODunzps7A9T06
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Use temp files in push_to_hub to save memory
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write parquet data to temp files on disk prior to upload to save memory
this is enabled for for datasets loaded using streaming=True/False
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PR_kwDODunzps7AmQfP
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Fix 4910 kwargs
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Fix #4910 : Merge duplicate kwargs in `load_dataset_builder()`
Problem: load_dataset("dataset", base_path="./data")` gives TypeError("multiple values for keyword 'base_path')
Fix: {**builder_kwargs, **config_kwargs} to user kwargs override dataset defaults
Repro : python
Before: TypeError
load_dataset("rotten_tomatoes", base_path="./sample_data")
After: Works
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PR_kwDODunzps7AkMoM
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Updated get_dataset_config_names returning default in offline mode
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When a dataset is cached and accessed in offline mode, get_dataset_config_names was returning default instead of the actual cached config names. This happened because CachedDatasetModuleFactory.get_module returned a DatasetModule without builder_configs_parameters, causing the fallback to default in get_dataset_config_names.
The fix reads config_name from each dataset_info file in the cache directory and includes them as builder_configs_parameters in the returned DatasetModule. Invalid or missing dataset_info.json files are handled.
**Testing:**
1. Download a dataset in online mode so it gets cached
2. Switch to offline mode and call get_dataset_config_names
3. Verify it returns the cached config names instead of ['default']
**Example:**
- HF_DATASETS_OFFLINE=0 HF_HOME="/tmp/hftemp" python -c "import datasets; datasets.load_dataset('cais/mmlu', 'all')"
- HF_DATASETS_OFFLINE=1 HF_HOME="/tmp/hftemp" python -c "import datasets; print(datasets.get_dataset_config_names('cais/mmlu'))"
- -> Expected output: ['all']
Fixes https://github.com/huggingface/datasets/issues/7947
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PR_kwDODunzps7Aj2hP
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Write image/audio/video blobs as is in parquet (PLAIN)
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following #7971
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PR_kwDODunzps7AikAO
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Docs: add Dataset.from_dict example
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Docs: add a minimal `Dataset.from_dict` example.
This helps new users discover the most direct way to build a small dataset from in-memory Python data.
Docs-only change.
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PR_kwDODunzps7Aij_g
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Fix duplicate kwargs in load_dataset_builder
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Avoid passing duplicate keyword arguments to `load_dataset_builder`.
Some module factories provide values in `builder_kwargs` (e.g. `base_path`), and users can also pass the same keys via `config_kwargs`, which raises:
`TypeError: ... got multiple values for keyword argument ...`.
Fix: if `config_kwargs` is provided, drop overlapping keys from `builder_kwargs` (keep the user-provided values).
Includes a regression test.
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PR_kwDODunzps7AiMd8
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Fix resolve_pattern for local symlinked files
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Fix `resolve_pattern` for *local symlinked files*.
Problem: on the local `file://` filesystem, `fsspec` can report symlinks as `type=="other"` and omit the `islink` flag, so symlinked files are skipped.
Fix: when `protocol=="file"`, treat `os.path.islink(filepath)` as a link candidate and include it if it resolves to a regular file.
Includes a regression test in `tests/test_data_files.py`.
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PR_kwDODunzps7AT07I
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feat: implement iter_arrow for skip, take and step iterables
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This commit optimizes streaming operations by implementing [_iter_arrow](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:377:4-391:57) for [SkipExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:1798:0-1892:42), [TakeExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:1944:0-2048:42), and [StepExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:667:0-743:42).
### Key Changes:
- **Fast Batch Processing**: Enabled batch-level slicing for `.skip(n)` and `.take(n)` on streaming datasets, bypassing slow row-by-row iteration.
- **Optimized Sharding**: Updated [StepExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:667:0-743:42) (used in distributed training) to use Arrow's `.take()` to extract multiple records from a batch simultaneously.
- **State Preservation**: Reinforced [_init_state_dict](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:505:4-514:31) and [load_state_dict](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:2382:4-2430:46) to support flawless checkpointing and resumption while using Arrow iteration.
### Performance Impact:
Users will experience significant performance gains when skipping or taking examples in streaming mode. By staying in the "Arrow path" and avoiding Python dictionary conversions, data loading overhead is drastically reduced, especially for large-scale training jobs.
### Testing:
Integrated 6 new unit tests into [tests/test_iterable_dataset.py](cci:7://file:///c:/Users/ASUS/Desktop/datasets/tests/test_iterable_dataset.py:0:0-0:0) to verify:
- Functional correctness for [skip](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:3149:4-3191:9), [take](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:3236:4-3271:9), and `step` using Arrow iteration.
- Reliable state checkpointing and resumption after partial iteration.
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push_to_hub() for videos
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possible now that row group sizes are auto-determined based on the content size after https://github.com/huggingface/datasets/pull/7589
Videos are uploaded as PLAIN in Parquet to make sure they can be seeked remotely and with random access to frames in https://github.com/huggingface/datasets/pull/7976
In the future it could be cool to have the same behavior as when videos are separate files, i.e. lazily load them instead of downloading them completely in streaming mode.
Right now there is this discrepency:
- `load_dataset("username/my-folder-of-videos", streaming=True)` -> videos are lazy loaded one by one when iterating, and only actually downloaded when accessing frames in `torchcodec`
- `load_dataset("username/my-video-dataset-in-parquet", streaming=True)` -> videos are downloaded one by one when iterating, even if no frame is accessed in `torchcodec`
close https://github.com/huggingface/datasets/issues/7493
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I_kwDODunzps7mpvMC
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cast_column(..., Audio) fails with load_dataset("csv",)
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[
"The following code *does* work:\n```py\nfrom datasets import load_dataset,Audio,Features\n\ndataset = load_dataset(\"csv\",data_files=\"audio.csv\",features=Features({\"audio\": Audio()}))\nprint(dataset[\"train\"][0][\"audio\"])\n```",
"Thanks for reporing ! Are you using pandas v3 by any chance ? The CSV loader uses pandas and this release is brand new and might have caused a breaking change",
"pandas 3.0.0 was present but I've also reproduced the issue with pandas 2.3.3."
] | 2026-01-29T09:33:35
| 2026-01-29T22:24:14
| 2024-08-21T09:35:06
|
NONE
| null | null | null | null |
### Describe the bug
Attempt to load a dataset from a csv with a single `audio` column with a single row with a path to an audio file fails when casting the column to Audio, but the exact same dataset created from a dictionary succeeds.
### Steps to reproduce the bug
1. Have any valid audio file `audio.wav`
2. Have a csv file named `audio.csv` with the following content:
```csv
"audio"
"audio.wav"
```
3. Attempt to execute the following python code:
```py
from datasets import load_dataset,Audio,Dataset
dataset = Dataset.from_dict({"audio": ["audio.wav"]})
dataset = dataset.cast_column("audio", Audio())
print(dataset[0]["audio"])
# ^^ succeeds with output: <datasets.features._torchcodec.AudioDecoder object at 0x7a32b341a3c0>
dataset = load_dataset("csv", data_files="audio.csv")
dataset = dataset.cast_column("audio", Audio())
# ^^ errors and terminates
print(dataset[0]["audio"])
```
The error is:
```pytb
Traceback (most recent call last):
File "~/datasets-bug/explore.py", line 8, in <module>
dataset = dataset.cast_column("audio", Audio(sampling_rate=24000))
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/dataset_dict.py", line 337, in cast_column
return DatasetDict({k: dataset.cast_column(column=column, feature=feature) for k, dataset in self.items()})
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/fingerprint.py", line 468, in wrapper
out = func(dataset, *args, **kwargs)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/arrow_dataset.py", line 2201, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1124, in cast
return MemoryMappedTable(table_cast(self.table, *args, **kwargs), self.path, replays)
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
return feature.cast_storage(array)
~~~~~~~~~~~~~~~~~~~~^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/features/audio.py", line 272, in cast_storage
return array_cast(storage, self.pa_type)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1949, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1147, in pyarrow.lib.Array.cast
File "~/datasets-bug/.venv/lib/python3.14/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from large_string to struct using function cast_struct
```
### Expected behavior
The audio column with file paths loaded from a csv can be converted to AudioDecoder objects the same as an identical dataset created from a dict.
### Environment info
datasets 4.3.0 and 4.5.0, Ubuntu 24.04 amd64, python 3.13.11 and 3.14.2
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Count examples in lance
|
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] | 2026-01-28T12:00:37
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| 2026-01-28T13:00:23
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```python
In [1]: from datasets import load_dataset_builder, StreamingDownloadManager
In [2]: b = load_dataset_builder("lance-format/openvid-lance")
Resolving data files: 100%|█| 240/240 [00:00<00:00, 42675.64it/s
In [3]: b.count_examples(StreamingDownloadManager())
Out[3]: {'train': 937957}
```
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I_kwDODunzps7mXwrD
| 7,968
|
Potential conflicting type checks and dead code in `/src/datasets/table.py`
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[
"ConcatenationTable is a subclass of datasets.table.Table but not pa.Table, so it should be fine"
] | 2026-01-28T11:34:53
| 2026-01-28T13:05:28
| 2024-08-21T09:35:06
|
NONE
| null | null | null | null |
When statically analyzing and manually reviewing the code, I noticed a potential logic conflicting in `/src/datasets/table.py` as follows:
```
def to_blocks(table: Union[pa.Table, Table]) -> list[list[TableBlock]]:
if isinstance(table, pa.Table):
return [[InMemoryTable(table)]]
elif isinstance(table, ConcatenationTable): # dead code
return copy.deepcopy(table.blocks)
else:
return [[table]]
```
Within the function, the condition `isinstance(table, ConcatenationTable)` at line 4 will never be True because the previous condition `isinstance(table, pa.Table)` at line 2 would have already caught all instances of `ConcatenationTable` (since `ConcatenationTable` is a subtype of `pa.Table`). This creates a logical conflict in the type checking flow.
Please verify if this logic is intentional or it is an issue warranting a refactoring or fixing.
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Issue 7756 Fix - multiprocessing hang issue with start method check
|
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[] | 2026-01-28T05:02:20
| 2026-01-31T18:26:03
| 2024-08-21T09:35:06
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Added a fix to prevent multiprocessing hangs by checking the start method.
Detects bad multiprocessing start_method, fallback happens.
https://github.com/huggingface/datasets/issues/7756
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PR_kwDODunzps6_qp_e
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Infer types from lance blobs
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] | 2026-01-27T18:00:25
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Ex: infer Video() type in https://huggingface.co/datasets/lance-format/openvid-lance and Image() type in https://huggingface.co/datasets/lance-format/laion-1m
```python
from datasets import load_dataset
ds = load_dataset("lance-format/laion-1m", streaming=True, split="train")
print(ds.feature["image"])
# Image()
ds = load_dataset("lance-format/openvid-lance", streaming=True, split="train")
print(ds.feature["video_blob"])
# Video()
```
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I_kwDODunzps7l-8ld
| 7,965
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`huggingface_hub.errors.HfHubHTTPError: 404 Client Error: Not Found for url` when fetching a dataset with `datasets.load_dataset`
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[
"Hi ! Yes you should use `cornell-movie-review-data/rotten_tomatoes` instead of `rotten_tomatoes`, which is the legacy name. Those datasets have been moved under their actual owners accounts some time ago (but we were keeping the old names as aliases)\n\nSome other impacted names are:\n- `imdb` -> `stanfordnlp/imdb`\n- `wikitext` -> `Salesforce/wikitext`\n- `gsm8k` -> `openai/gsm8k`\n- `winogrande` -> `allenai/winogrande`\n\nWe're working on re-enabling them as aliases for backward compatibility. I'll post updates here, sorry for the inconvenience.\n\n**Using the actual name instead of the old legacy name is more future proof though**",
"Thanks for the heads up @lhoestq ! fyi, this change is likely breaking a lot of repos that have legacy names hardcoded ([example](https://github.com/allenai/olmes/pull/40)) Would be helpful to many to share this update in a more visible way if it is likely to persist for a while.",
"[internal tracking link](https://github.com/huggingface-internal/moon-landing/pull/16539)",
"@lhoestq Thanks for clarifying!",
"The aliases are re-enabled :)",
"Thanks!",
"Can I close this issue?",
"Yep :)"
] | 2026-01-27T02:20:31
| 2026-01-28T15:14:50
| 2026-01-28T15:14:50
|
NONE
| null | null | null | null |
Not a bug but a question. We started getting the following error:
https://github.com/mlflow/mlflow/actions/runs/21368603305/job/61506951617
```
ests/data/test_huggingface_dataset_and_source.py::test_from_huggingface_dataset_constructs_expected_dataset_with_revision - huggingface_hub.errors.HfHubHTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/rotten_tomatoes/revision/aa13bc287fa6fcab6daf52f0dfb9994269ffea28 (Request ID: Root=1-6977aeca-35bc2b5b605884926a9224d0;aa2391f3-26e8-4975-b9bb-114b2fa40223)
```
Adding a user id fixed the issue (https://github.com/mlflow/mlflow/pull/20350). `https://huggingface.co/api/datasets` no longer accepts a name-only path like `rotten_tomatoes`? Just wondering what changed. Thanks!
|
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PR_kwDODunzps6_eOZR
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handle blob lance
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following #7913
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| 3,856,921,322
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PR_kwDODunzps6_ajTD
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Support null in json string cols
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| 2026-01-26T17:48:46
| 2026-01-26T17:48:44
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fix for https://huggingface.co/datasets/arcprize/arc_agi_v2_public_eval
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PR_kwDODunzps6_aM71
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Use Sequence instead of list in Dataset.from_parquet type hints
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[
"Thank you for the review!\r\n\r\nI’ve updated the implementation to:\r\n- Use Sequence from collections.abc as per project conventions\r\n- Restore backward compatibility with Union[PathLike, Sequence[PathLike]]\r\n- Keep the columns annotation as Optional[Sequence[str]]\r\n\r\nThe fixes are pushed. Please let me know if anything else is needed.\r\n\r\n"
] | 2026-01-26T17:01:47
| 2026-02-04T06:47:52
| 2024-08-21T09:35:06
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NONE
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This PR updates type annotations in Dataset.from_parquet to use Sequence instead of list to avoid mypy invariant type issues, as discussed in issue #5354.
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PR_kwDODunzps6-9GHz
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Revert "feat: avoid some copies in torch formatter (#7787)"
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This reverts commit c412a6f5a50955e141c5169bf7abe005d10228d2 (I assume it was ai generated which makes it hard for me to review and make sure it doesn't have bad edge cases, but lmk if it wasn't, anyways it didn't take into account the torch kwargs which are responsible for sending the data to the correct device)
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docs: fix grammar and add type hints in splits.py
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This PR improves the documentation in
src/datasets/splits.py
by:
Fixing pluralization/grammar errors in docstrings (Lines 62, 73, 403).
Adding Python type hints to the
NamedSplit
constructor for better code quality.
Verified with ruff format and ruff check.
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docs: fix typo in arrow_dataset.py comment
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I_kwDODunzps7lT2AI
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[CUDA Tensors Not working in ~v4.5.0] set_format(type="torch", device="cuda") returns cpu
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[
"@lhoestq tagging you here as you were on the previous issue, hope that's fine. ",
"Thanks for reporting, let me take a look",
"I reverted this change which caused the issue #7961 , I'll do a new release soon but in the meantime feel free to install `datasets` from source",
"@lhoestq thanks a lot. I am actually checking older versions of datasets and it seems that it doesn't work with 4.2.0 as well.\n\n(hopefully that's relevant)."
] | 2026-01-23T12:06:48
| 2026-01-25T05:30:53
| 2024-08-21T09:35:06
|
NONE
| null | null | null | null |
### Describe the bug
The problem is that when calling:
```ds.set_format(type="torch", columns = ["input", "labels"], device="cuda")```
The device type of the individual datapoints is now: `cpu` as opposed to `cuda:0`.
With `v4.0.0` it still works. With `v4.5.0` it doesn't work anymore.
Related Issue:
https://github.com/huggingface/datasets/issues/1762
### Steps to reproduce the bug
Steps to reproduce the bug:
```
ds.set_format(type="torch", columns = ["input", "labels"], device="cuda")
print(ds["train"][0]["input"].device) #outputs cpu
```
The above should be `cuda:0`.
### Expected behavior
`set_format` should be able to move data to GPU.
### Environment info
datasets==4.5.0
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Fix all exhausted without replacement
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[
"Hi maintainers 👋 \r\nThis PR fixes `all_exhausted_without_replacement` to ensure all samples are emitted exactly once, \r\nand adds a regression test reproducing the reported issue.\r\n\r\nHappy to adjust the implementation or split changes if you’d prefer.\r\nThanks!\r\n",
"We just merged https://github.com/huggingface/datasets/pull/7955 which also fix the issue, thanks for investigating !\r\n\r\nI'm closing this one if you don't mind"
] | 2026-01-21T18:47:32
| 2026-01-23T16:09:28
| 2026-01-23T16:09:28
|
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Fix interleave_datasets "all_exhausted_without_replacement" stopping strategy
- Corrected logic to ensure each sample is picked exactly once when using
stopping_strategy="all_exhausted_without_replacement".
- Adjusted boolean stopping condition to properly track dataset exhaustion.
- Added test to verify the last element is included as expected.
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I_kwDODunzps7k08AC
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|
Is the 10k files / folder limit a hard limit for a dataset repo?
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[
"Yes, that's a hard limit. Can you split your files into different folders? Or we'll probably have a new repo type in the near to mid future that will relax this limit a bit. ",
"Thanks! Working around this with a different sharding parameter to have fewer overall fragments (and therefore fewer files in `*.lance/data/` and `*.lance/_transactions/`"
] | 2026-01-21T16:37:38
| 2026-01-22T09:38:38
| 2024-08-21T09:35:06
|
NONE
| null | null | null | null |
### Feature request
Can the hard limit of 10k files per folder be extended with acceptable loss in performance?
### Motivation
I'm uploading a lance dataset to huggingface hub and have a folder inside lance internals (`data/*.lance/_transactions`) that has > 20k atomic transaction records and my commits are being rejected.
```
Bad request for commit endpoint:
Your push was rejected because it contains too many files per directory. Each directory in your git repo can only contain up to 10000 files. Offending reference: refs/heads/main Offending directories: /data/expression.lance/_transactions/
```
### Your contribution
Open to suggestions for how to make a PR
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Fix interleave_datasets with all_exhausted_without_replacement strategy
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7955). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-21T08:10:45
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When using interleave_datasets with stopping_strategy="all_exhausted_without_replacement" and probabilities=None, the function was incorrectly falling into the undersampling branch, causing it to stop at min(lengths) instead of continuing until all datasets were exhausted.
This fix adds a specific branch to handle the all_exhausted_without_replacement case when probabilities=None. The new logic cycles through all datasets round by round, adding elements from each dataset until all are exhausted, ensuring each element appears exactly once.
Example fix:
- Input: d1=[0,1,2], d2=[10,11,12,13], d3=[20,21,22]
- Before: [0, 10, 20, 1, 11, 21, 2, 12, 22]
- After: [0, 10, 20, 1, 11, 21, 2, 12, 22, 13]
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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I_kwDODunzps7ktEe5
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all_exhausted_without_replacement working same as first_exhausted
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| 2026-01-21T08:13:00
| 2024-08-21T09:35:06
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CONTRIBUTOR
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### Describe the bug
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted_without_replacement")
>>> dataset["a"][:100]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
expected output: [0, 10, 20, 1, 11, 21, 2, 12, 22,13]
datasets version 4.5.0
### Steps to reproduce the bug
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted_without_replacement")
>>> dataset["a"][:100]
### Expected behavior
[0, 10, 20, 1, 11, 21, 2, 12, 22,13]
### Environment info
datasets version 4.5.0
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#5354: replace list with Sequence in from_parquet type hints
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\This PR replaces `list` type hints with `Sequence` in `from_parquet` to improve type checking.
Note: Local pytest errors on Python 3.13 due to removal of `distutils` are unrelated to this change.
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Fix #5354: replace list with Sequence in from_parquet type hints
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This PR replaces `list` type hints with `Sequence` in `from_parquet` to improve type checking.
Note: Local pytest errors on Python 3.13 due to removal of `distutils` are unrelated to this change.
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feat: Add GenBank file format support for biological sequence data
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| 2024-08-21T09:35:06
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## Summary
Add native support for loading GenBank (.gb, .gbk, .genbank) files, a standard format for biological sequence data with annotations maintained by NCBI.
## Changes
- Add `genbank` packaged module with pure Python state machine parser
- Register GenBank extensions in `_PACKAGED_DATASETS_MODULES` and `_EXTENSION_TO_MODULE`
- Add comprehensive test suite (28 tests)
## Features
- **Metadata parsing**: LOCUS, DEFINITION, ACCESSION, VERSION, KEYWORDS, ORGANISM, taxonomy
- **Feature parsing**: Structured JSON output with location parsing (complement, join)
- **Sequence parsing**: ORIGIN section with automatic length calculation
- **Compression support**: gzip, bz2, xz via magic bytes detection
- **Memory efficiency**: Dual-threshold batching (batch_size + max_batch_bytes)
- **Large sequences**: Uses `large_string` Arrow type for sequences/features
## Usage
```python
from datasets import load_dataset
# Load GenBank files
ds = load_dataset("genbank", data_files="sequences.gb")
# With options
ds = load_dataset("genbank", data_files="*.gbk",
columns=["sequence", "organism", "features"],
parse_features=True)
```
## Test plan
- [x] All 28 unit tests pass
- [x] Tests cover: basic loading, multi-record, compression, feature parsing, column filtering, batching, schema types
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Add examples for Lance datasets
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7950). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Hi @prrao87 ! All the main examples with pylance should be in their dedicated documentation page IMO. Let's document Lance as a supported format in the `datasets` docs, and move the `pylance` docs and examples there instead if you don't mind: https://github.com/huggingface/hub-docs/pull/2164\r\n\r\nI think for the `datasets` docs we can simply make sure Lance is mentioned in the lists of supported formats, and have a section in the Video documentation pages alongside WebDataset for example:\r\n\r\n* [video_load.md](https://huggingface.co/docs/datasets/video_load) for loading \r\n* [video_dataset.md](https://huggingface.co/docs/datasets/video_dataset) for dataset creation\r\n* [loading_methods.md](https://huggingface.co/docs/datasets/package_reference/loading_methods) the reference which lists all loading methods and configs",
"Hi @lhoestq, what do you mean by this?\r\n> Let's document Lance as a supported format in the datasets docs\r\n\r\nWhere exactly would Lance be mentioned? I wasn't able to find a section titled \"supported formats\" in the docs. The rest of the parts of your response made sense (can make those updates). Thanks!",
"Hi @lhoestq, I've updated the docs in various places, to show how to use Lance for image creation/loading and also video creation/loading. I've also removed the `use_with_lance.mdx` page's contents over to the Hub docs by enhancing that page, as you mentioned.\r\n\r\nI think this should cover all your feedback points! Please let me know if anything's missing. I'll be back in a future PR with audio examples, I think the HF community would benefit from using Lance format for audio storage/retrieval, too.",
"Thanks! We'll get those fixed and send more PRs ;)"
] | 2026-01-17T19:33:31
| 2026-01-23T16:40:20
| 2026-01-23T16:16:43
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## Summary
Updated the Lance integration docs to match the official dataset cards and expand coverage of multimodal workflows in `use_with_lance.mdx`.
## Details
- Added a Lance format-focused guidance page for multimodal examples:
- Stream from the Hub via `datasets` API
- Use Lance's dataset API to pass in a `hf://...` path specifier,
- Scan, vector search, schema evolution examples
- "Export to Lance" example for materializing filtered subsets locally
- Aligned all dataset names/paths to existing datasets so users can immediately test it out: `lance-format/laion-1m` (image) and the `lance-format/openvid-lance` (video)
Depending on the questions we get from users, we may add more descriptive examples as we along, but this should be a good start!
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docs: clarify documentation build instructions
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json: add optional return_file_name parameter
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This PR adds an optional `return_file_name` parameter to the JSON dataset loader.
When enabled, a new `file_name` column is added containing the source file name
for each row. Default behavior is unchanged.
Changes:
- Add `return_file_name` to JsonConfig
- Append file name during JSON table generation
- Add tests covering default and enabled behavior, and ensures other functions are not affected
Motivation:
This helps resume training from checkpoints by identifying already-consumed data shards.
Fixes #5806
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