From 0c3d8b0954117e8e6996f54489210f9e9d5ceced Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 15 Nov 2023 16:49:35 +0100
Subject: [PATCH] Update FDS README (#2605)

---
 datasets/README.md | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/datasets/README.md b/datasets/README.md
index f4b3fab73d8e..876b6f453fa5 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -3,7 +3,7 @@
 [![GitHub license](https://img.shields.io/github/license/adap/flower)](https://github.com/adap/flower/blob/main/LICENSE)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
-![Downloads](https://pepy.tech/badge/flwr)
+![Downloads](https://pepy.tech/badge/flwr-datasets)
 [![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
 
 Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework. 
@@ -22,7 +22,13 @@ Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets i
 * Arrow.
 
 Create **custom partitioning schemes** or choose from the **implemented partitioning schemes**:
+* Partitioner (the abstract base class) `Partitioner`
 * IID partitioning `IidPartitioner(num_partitions)`
+* Natural ID partitioner `NaturalIdPartitioner`
+* Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) `SizePartitioner` 
+* Linear partitioner `LinearPartitioner`
+* Square partitioner `SquarePartitioner`
+* Exponential partitioner `ExponentialPartitioner`
 * more to come in future releases.
 
 # Installation
@@ -53,9 +59,9 @@ If you plan to change the type of the dataset to run the code with your ML frame
 
 # Usage
 
-The Flower Datasets exposes `FederatedDataset(dataset, partitioners)` abstraction to represent the dataset needed for federated learning/analytics. It has two powerful methods that let you handle the dataset preprocessing. They are `load_partition(node_id, split)` and `load_full(split)`.
+Flower Datasets exposes the `FederatedDataset` abstraction to represent the dataset needed for federated learning/evaluation/analytics. It has two powerful methods that let you handle the dataset preprocessing: `load_partition(node_id, split)` and `load_full(split)`.
 
-Here's a quick example of how to partition the MNIST dataset:
+Here's a basic quickstart example of how to partition the MNIST dataset:
 
 ```
 from flwr_datasets import FederatedDataset
@@ -68,23 +74,17 @@ mnist_partition_0 = mnist_fds.load_partition(0, "train")
 centralized_data = mnist_fds.load_full("test")
 ```
 
-`FederatedDataset(dataset, partitioners)` allows you specification of:
-
-* `dataset:str` - the name of the dataset.
-
-* `partitioners: Dict[str: int]` - `{split_name: str` to `number-of-partitions: int}` - partitioner that will be used with an associated split of the dataset e.g. `{"train": 100}`. It assumes by default the i.i.d. partitioning.
-
-More customization of `partitioners` is coming in future releases. 
+For more details, please refer to the specific how-to guides or tutorial. They showcase customization and more advanced features.
 
 # Future release
 
 Here are a few of the things that we will work on in future releases:
 
-* Support for more datasets (especially the ones that have user id present).
-* Creation of custom `Partitioner`s.
-* More out-of-the-box `Partitioner`s.
-* Passing `Partitioner`s via `FederatedDataset`'s `partitioner` argument. 
-* Customization of the dataset splitting before the partitioning.
+* ✅ Support for more datasets (especially the ones that have user id present).
+* ✅ Creation of custom `Partitioner`s.
+* ✅ More out-of-the-box `Partitioner`s.
+* ✅ Passing `Partitioner`s via `FederatedDataset`'s `partitioners` argument. 
+* ✅ Customization of the dataset splitting before the partitioning.
 * Simplification of the dataset transformation to the popular frameworks/types.
 * Creation of the synthetic data,
 * Support for Vertical FL.