helmo/github-issues huggingface dataset:
Optimized sequence encoding for scalars
State: closed | Author: lukasgd (CONTRIBUTOR)

The change in https://github.com/huggingface/datasets/pull/3197 introduced redundant list-comprehensions when `obj` is a long sequence of scalars. This becomes a noticeable overhead when loading data from an `IterableDataset` in the function `_apply_feature_types_on_example` and can be eliminated by adding a check for scalars in `encode_nested_example` proposed here. 

In the following code example
```
import time
from datasets.features import Sequence, Value
from datasets.features.features import encode_nested_example

schema = Sequence(Value("int32"))
obj = list(range(100000))

start = time.perf_counter()
result = encode_nested_example(schema, obj)
stop = time.perf_counter()

print(f"Time spent is {stop-start} sec")
```
`encode_nested_example` becomes 492x faster (from 0.0769 to 0.0002 sec), respectively 322x (from 0.00814 to 0.00003 sec) for a list of length 10000, on a GH200 system, making it unnoticeable when loading data with tokenization.

Another change is made to avoid creating arrays from scalars and afterwards re-extracting them during casting to python (`obj == obj.__array__()[()]` in that case), which avoids a regression in the array write benchmarks.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7393). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
push_to_hub payload too large error when using large ClassLabel feature
State: open | Author: DavidRConnell (NONE)

### Describe the bug

When using `datasets.DatasetDict.push_to_hub` an `HfHubHTTPError: 413 Client Error: Payload Too Large for url` is raised if the dataset contains a large `ClassLabel` feature. Even if the total size of the dataset is small.

### Steps to reproduce the bug

``` python
import random
import sys

import datasets

random.seed(42)


def random_str(sz):
    return "".join(chr(random.randint(ord("a"), ord("z"))) for _ in range(sz))


data = datasets.DatasetDict(
    {
        str(i): datasets.Dataset.from_dict(
            {
                "label": [list(range(3)) for _ in range(10)],
                "abstract": [random_str(10_000) for _ in range(10)],
            },
        )
        for i in range(3)
    }
)
features = data["1"].features.copy()
features["label"] = datasets.Sequence(
    datasets.ClassLabel(names=[str(i) for i in range(50_000)])
)
data = data.map(lambda examples: {}, features=features)

feat_size = sys.getsizeof(data["1"].features["label"].feature.names)
print(f"Size of ClassLabel names: {feat_size}")
# Size of ClassLabel names: 444376


data.push_to_hub("dconnell/pubtator3_test")
```

Note that this succeeds if `ClassLabel` has fewer names or if `ClassLabel` is replaced with `Value("int64")`


### Expected behavior

Should push the dataset to hub.

### Environment info

Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-126-generic-x86_64-with-glibc2.35
- Python version: 3.12.8
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0


Comments: See also <https://discuss.huggingface.co/t/datasetdict-push-to-hub-failing-with-payload-to-large/140083/8>
Re-add py.typed
State: open | Author: NeilGirdhar (CONTRIBUTOR)
Labels: name: enhancement

### Feature request

The motivation for removing py.typed no longer seems to apply.  Would a solution like [this one](https://github.com/huggingface/huggingface_hub/pull/2752) work here?

### Motivation

MyPy support is broken.  As more type checkers come out, such as RedKnot, these may also be broken.  It would be good to be PEP 561 compliant as long as it's not too onerous.

### Your contribution

I can re-add py.typed, but I don't know how to make sur all of the `__all__` files are provided (although you may not need to with modern PyRight).
Getting statistics about filtered examples
State: closed | Author: jonathanasdf (NONE)

@lhoestq wondering if the team has thought about this and if there are any recommendations?

Currently when processing datasets some examples are bound to get filtered out, whether it's due to bad format, or length is too long, or any other custom filters that might be getting applied. Let's just focus on the filter by length for now, since that would be something that gets applied dynamically for each training run. Say we want to show a graph in W&B with the running total of the number of filtered examples so far.

What would be a good way to go about hooking this up? Because the map/filter operations happen before the DataLoader batches are created, at training time if we're just grabbing batches from the DataLoader then we won't know how many things have been filtered already. But there's not really a good way to include a 'num_filtered' key into the dataset itself either because dataset map/filter process examples independently and don't have a way to track a running sum.

The only approach I can kind of think of is having a 'is_filtered' key in the dataset, and then creating a custom batcher/collator that reads that and tracks the metric?

Comments: You can actually track a running sum in map() or filter() :)

```python
num_filtered = 0

def f(x):
    global num_filtered
    condition = len(x["text"]) < 1000
    if not condition:
        num_filtered += 1
    return condition

ds = ds.filter(f)
print(num_filtered)
```

and if you want to use multiprocessing, make sure to use a variable that is shared across processes


```python
from multiprocess import Manager

manager = Manager()
num_filtered = manager.Value('i', 0)

def f(x):
    global num_filtered
    condition = len(x["text"]) < 1000
    if not condition:
        num_filtered.value += 1
    return condition

ds = ds.filter(f, num_proc=4)
print(num_filtered.value)
```

PS: `datasets` uses `multiprocess` instead of the `multiprocessing` package to support lambda functions in map() and filter()

Oh that's great to know!

I guess this value would not be exactly synced with the batch in cases of pre-fetch and shuffle buffers and so on, but that's probably fine. Thanks!
OSError: [Errno 22] Invalid argument forbidden character
State: closed | Author: langflogit (NONE)

### Describe the bug

I'm on Windows and i'm trying to load a datasets but i'm having title error because files in the repository are named with charactere like < >which can't be in a name file. Could it be possible to load this datasets but removing those charactere ?

### Steps to reproduce the bug

load_dataset("CATMuS/medieval") on Windows

### Expected behavior

Making the function to erase the forbidden character to allow loading the datasets who have those characters. 

### Environment info

- `datasets` version: 3.2.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.12.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0

Comments: You can probably copy the dataset in your HF account and rename the files (without having to download them to your disk). Or alternatively feel free to open a Pull Request to this dataset with the renamed file

Thank you, that will help me work around this problem
Dynamic adjusting dataloader sampling weight
State: open | Author: whc688 (NONE)

Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again.

Comments: You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes

Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it

it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic
Add bookfolder Dataset Builder for Digital Book Formats
State: closed | Author: shikanime (NONE)
Labels: name: enhancement

### Feature request

This feature proposes adding a new dataset builder called bookfolder to the datasets library. This builder would allow users to easily load datasets consisting of various digital book formats, including: AZW, AZW3, CB7, CBR, CBT, CBZ, EPUB, MOBI, and PDF.

### Motivation

Currently, loading datasets of these digital book files requires manual effort. This would also lower the barrier to entry for working with these formats, enabling more diverse and interesting datasets to be used within the Hugging Face ecosystem.

### Your contribution

This feature is rather simple as it will be based on the folder-based builder, similar to imagefolder. I'm willing to contribute to this feature by submitting a PR

Comments: On second thought, probably not a good idea.
Make IterableDataset (optionally) resumable
State: open | Author: yzhangcs (CONTRIBUTOR)

### What does this PR do?
This PR introduces a new `stateful` option to the `dataset.shuffle` method, which defaults to `False`. 
When enabled, this option allows for resumable shuffling of `IterableDataset` instances, albeit with some additional memory overhead.

Key points:
* All tests have passed
* Docstrings have been updated to reflect the new functionality

I'm very looking forward to receiving feedback on this implementation! @lhoestq

Comments: @lhoestq Hi again~ Just circling back on this
Wondering if there’s anything I can do to help move this forward. 🤗 
Thanks!

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7385). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Support async functions in map()
State: closed | Author: lhoestq (MEMBER)

e.g. to download images or call an inference API like HF or vLLM

```python
import asyncio
import random

from datasets import Dataset


async def f(x):
    await asyncio.sleep(random.random())

ds = Dataset.from_dict({"data": range(100)})
ds.map(f)
# Map: 100%|█████████████████████████████| 100/100 [00:01<00:00, 99.81 examples/s]
```

TODO
- [x] clean code (right now it's a big copy paste)
- [x] batched
- [x] Dataset.map()
- [x] IterableDataset.map()
- [x] Dataset.filter()
- [x] IterableDataset.filter()
- [x] test
- [x] docs

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7384). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

example of what you can do with it:

```python
import aiohttp
from huggingface_hub import get_token

from datasets import Dataset


API_URL = "https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct/v1/chat/completions"
PROMPT = "What is this text mainly about ? Here is the text:\n\n```\n{Problem}\n```\n\nReply in one or two words."

async def query(example):
    headers = {"Authorization": f"Bearer {get_token()}", "Content-Type": "application/json"}
    json = {"messages": [{"role": "user", "content": PROMPT.format(Problem=example["Problem"])}], "max_tokens": 20, "seed": 42}
    async with aiohttp.ClientSession() as session, session.post(API_URL, headers=headers, json=json) as response:
        output = await response.json()
        return {"output": output["choices"][0]["message"]["content"]}

ds = Dataset.from_dict({"Problem": ["1 + 1"] * 10})
ds = ds.map(query)
print(ds[0])
# {'Problem': '1 + 1', 'output': 'Arithmetic'}
```
Add Pandas, PyArrow and Polars docs
State: closed | Author: lhoestq (MEMBER)

(also added the missing numpy docs and fixed a small bug in pyarrow formatting)

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7382). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
fix: dill default for version bigger 0.3.8
State: closed | Author: sam-hey (NONE)

Fixes def log for dill version >= 0.3.9 
https://pypi.org/project/dill/

This project uses dill with the release of version 0.3.9 the datasets lib. 

Comments: `datasets` doesn't support `dill` 0.3.9 yet afaik since `dill` made some changes related to the determinism of dumps

It would be cool to investigate (maybe run the `datasets` test) with recent `dill` to see excactly what breaks and if we can make `dill` 0.3.9 work with `datasets`
Allow pushing config version to hub
State: open | Author: momeara (NONE)
Labels: name: enhancement

### Feature request

Currently, when datasets are created, they can be versioned by passing the `version` argument to `load_dataset(...)`. For example creating `outcomes.csv` on the command line

```
echo "id,value\n1,0\n2,0\n3,1\n4,1\n" > outcomes.csv
```
and creating it
```
import datasets
dataset = datasets.load_dataset(
    "csv",
    data_files ="outcomes.csv",
    keep_in_memory = True,
    version = '1.0.0')
```

The version info is stored in the `info` and can be accessed e.g. by `next(iter(dataset.values())).info.version`

This dataset can be uploaded to the hub with `dataset.push_to_hub(repo_id = "maomlab/example_dataset")`. This will create a dataset  on the hub with the following in the `README.md`, but it doesn't upload the version information:

```
---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: value
    dtype: int64
  splits:
  - name: train
    num_bytes: 64
    num_examples: 4
  download_size: 1332
  dataset_size: 64
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
```

However, when I download from the hub, the version information is missing:

```
dataset_from_hub_no_version = datasets.load_dataset("maomlab/example_dataset")
next(iter(dataset.values())).info.version
```

I can add the version information manually to the hub, by appending it to the end of config section:

```
...
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  version: 1.0.0
---
```

And then when I download it, the version information is correct.





### Motivation

### Why adding version information for each config makes sense
  1. The version information is already recorded in the dataset config info data structure and is able to parse it correctly, so it makes sense to sync it with `push_to_hub`. 
  2. Keeping the version info in at the config level is different from version info at the branch level. As the former relates to the version of the specific dataset the config refers to rather than the version of the dataset curation itself.

## A explanation for the current behavior:
In [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
), the `_INCLUDED_INFO_IN_YAML` variable doesn't include `"version"`.

If my reading of the code is right, adding `"version"` to `_INCLUDED_INFO_IN_YAML`, would allow the version information to be uploaded to the hub.

### Your contribution

Request: add `"version"` to `_INCLUDE_INFO_IN_YAML` in [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
)


Comments: Hi ! This sounds reasonable to me, feel free to open a PR :)
Support for sparse arrays with the Arrow Sparse Tensor format?
State: open | Author: JulesGM (NONE)
Labels: name: enhancement

### Feature request

AI in biology is becoming a big thing. One thing that would be a huge benefit to the field that Huggingface Datasets doesn't currently have is native support for **sparse arrays**. 


Arrow has support for sparse tensors. 
https://arrow.apache.org/docs/format/Other.html#sparse-tensor


It would be a big deal if Hugging Face Datasets supported sparse tensors as a feature type, natively. 


### Motivation

This is important for example in the field of transcriptomics (modeling and understanding gene expression), because a large fraction of the genes are not expressed (zero). More generally, in science, sparse arrays are very common, so adding support for them would be very benefitial, it would make just using Hugging Face Dataset objects a lot more straightforward and clean.


### Your contribution

We can discuss this further once the team comments of what they think about the feature, and if there were previous attempts at making it work, and understanding their evaluation of how hard it would be. My intuition is that it should be fairly straightforward, as the Arrow backend already supports it.

Comments: Hi ! Unfortunately the Sparse Tensor structure in Arrow is not part of the Arrow format (yes it's confusing...), so it's not possible to use it in `datasets`. It's a separate structure that doesn't correspond to any type or extension type in Arrow.

The Arrow community recently added an extension type for fixed shape tensors at https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor, it should be possible to contribute an extension type for sparse tensors as well.
vllm批量推理报错
State: open | Author: YuShengzuishuai (NONE)

### Describe the bug

![Image](https://github.com/user-attachments/assets/3d958e43-28dc-4467-9333-5990c7af3b3f)

### Steps to reproduce the bug

![Image](https://github.com/user-attachments/assets/3067eeca-a54d-4956-b0fd-3fc5ea93dabb)

### Expected behavior

![Image](https://github.com/user-attachments/assets/77d32936-488f-4572-9365-bfb4170e555b)

### Environment info

![Image](https://github.com/user-attachments/assets/230335c4-825f-4db1-b07d-4776ef63ead8)

Comments: Make sure you have installed a recent version of `soundfile`
Excessive RAM Usage After Dataset Concatenation concatenate_datasets
State: open | Author: sam-hey (NONE)

### Describe the bug

When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.

https://github.com/huggingface/datasets/issues/2276

### Steps to reproduce the bug

```python
from datasets import  DatasetDict, concatenate_datasets

dataset = DatasetDict.load_from_disk("data")

...
...

combined_dataset = concatenate_datasets(
        [dataset[split] for split in dataset]
    )

#start SentenceTransformer training
```

### Expected behavior

I would not expect RAM utilization to increase after concatenation. Removing the concatenation step resolves the issue

### Environment info

sentence-transformers==3.1.1
datasets==3.2.0

python3.10

Comments: ![Image](https://github.com/user-attachments/assets/b6f8bcbd-44af-413e-bc06-65380eb0f746)

![Image](https://github.com/user-attachments/assets/a241fcd8-4b62-495c-926c-685f82015dfb)

Adding a img from memray
https://gist.github.com/sam-hey/00c958f13fb0f7b54d17197fe353002f

I'm having the same issue where concatenation seems to use a huge amount of RAM.

```python
# Load all chunks and concatenate them into a final dataset.
        chunk_datasets = [
            Dataset.load_from_disk(file, keep_in_memory=False)
            for file in tqdm(chunk_files, desc="Loading chunk datasets")
        ]
        logging.info("Concatenating chunk datasets...")
        final_dataset = concatenate_datasets(chunk_datasets)
```

This is a real issue for me as the final dataset is a few terabytes in size. I'm using datasets version `3.1.0`. Also tested with version `3.4.1`

I did have a short look, the error seems to be from `memory_map` and the stream not being closed. 

https://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/table.py#L48-L50


Did not have the time to test jet: https://github.com/sam-hey/datasets/tree/fix/concatenate_datasets

I will probably have a better look in a couple of days.
Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets
State: open | Author: gaohongkui (NONE)

### Description

I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue:

#### Code 1: Using `load_dataset`
```python
from datasets import Dataset, load_dataset

# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Load the DatasetDict
loaded_datasetdict = load_dataset("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 1350 samples.
- `test` has 150 samples.

#### Code 2: Using `load_from_disk`
```python
from datasets import Dataset, load_from_disk

# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Load the DatasetDict
loaded_datasetdict = load_from_disk("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 450 samples.
- `test` has 50 samples.

### Expected Behavior
I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly:
- `load_dataset` seems to merge all shards, resulting in a combined dataset.
- `load_from_disk` only loads the last saved dataset, ignoring previous shards.

### Questions
1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation?
2. If this is not intentional, could this be considered a bug?
3. What is the recommended way to handle cases where multiple datasets are saved to the same directory?


Thank you for your time and effort in maintaining this great library! I look forward to your feedback.
Support faster processing using pandas or polars functions in `IterableDataset.map()`
State: closed | Author: lhoestq (MEMBER)

Following the polars integration :)

Allow super fast processing using pandas or polars functions in `IterableDataset.map()` by adding support to pandas and polars formatting in `IterableDataset`

```python
import polars as pl
from datasets import Dataset

ds = Dataset.from_dict({"i": range(10)}).to_iterable_dataset()
ds = ds.with_format("polars")
ds = ds.map(lambda df: df.with_columns(pl.col("i").add(1).alias("i+1")), batched=True)
ds = ds.with_format(None)
print(next(iter(ds)))
# {'i': 0, 'i+1': 1}
```

It leverages arrow's zero-copy features from/to pandas and polars.

related to https://github.com/huggingface/datasets/issues/3444 #6762 

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7370). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

merging this and will make some docs and communications around using polars for optimizing data processing :)