semvec/pypi-packages huggingface dataset:
pyspark_huggingface 1.0.0
A DataSource for reading and writing HuggingFace Datasets in Spark
License: Apache-2.0
Requires Python: <4.0,>=3.9

<p align="center">
  <img alt="Hugging Face x Spark" src="https://pbs.twimg.com/media/FvN1b_2XwAAWI1H?format=jpg&name=large" width="352" style="max-width: 100%;">
  <br/>
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</p>

<p align="center">
    <a href="https://github.com/huggingface/pyspark_huggingface/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/pyspark_huggingface.svg"></a>
    <a href="https://huggingface.co/datasets/"><img alt="Number of datasets" src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/datasets&color=brightgreen"></a>
</p>

# Spark Data Source for Hugging Face Datasets

A Spark Data Source for accessing [🤗 Hugging Face Datasets](https://huggingface.co/datasets):

- Stream datasets from Hugging Face as Spark DataFrames
- Select subsets and splits, apply projection and predicate filters
- Save Spark DataFrames as Parquet files to Hugging Face
- Fully distributed
- Authentication via `huggingface-cli login` or tokens
- Compatible with Spark 4 (with auto-import)
- Backport for Spark 3.5, 3.4 and 3.3

## Installation

```
pip install pyspark_huggingface
```

## Usage

Load a dataset (here [stanfordnlp/imdb](https://huggingface.co/datasets/stanfordnlp/imdb)):

```python
import pyspark_huggingface
df = spark.read.format("huggingface").load("stanfordnlp/imdb")
```

Save to Hugging Face:

```python
# Login with huggingface-cli login
df.write.format("huggingface").save("username/my_dataset")
# Or pass a token manually
df.write.format("huggingface").option("token", "hf_xxx").save("username/my_dataset")
``` 

## Advanced

Select a split:

```python
test_df = (
    spark.read.format("huggingface")
    .option("split", "test")
    .load("stanfordnlp/imdb")
)
```

Select a subset/config:

```python
test_df = (
    spark.read.format("huggingface")
    .option("config", "sample-10BT")
    .load("HuggingFaceFW/fineweb-edu")
)
```

Filters columns and rows (especially efficient for Parquet datasets):

```python
df = (
    spark.read.format("huggingface")
    .option("filters", '[("language_score", ">", 0.99)]')
    .option("columns", '["text", "language_score"]')
    .load("HuggingFaceFW/fineweb-edu")
)
```

## Backport

While the Data Source API was introcuded in Spark 4, this package includes a backport for older versions.

Importing `pyspark_huggingface` patches the PySpark reader and writer to add the "huggingface" data source. It is compatible with PySpark 3.5, 3.4 and 3.3:

```python
>>> import pyspark_huggingface
huggingface datasource enabled for pyspark 3.x.x (backport from pyspark 4)
```

The import is only necessary on Spark 3.x to enable the backport.
Spark 4 automatically imports `pyspark_huggingface` as soon as it is installed, and registers the "huggingface" data source.