Backport data source to pyspark 3

README.md

@@ -1,2 +1,92 @@
-# pyspark_huggingface
-PySpark custom data source for Hugging Face Datasets
+<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/>
+  <br/>
+</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
+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.