Add README to Flower datasets (adap#2205)

datasets/README.md

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 # Flower Datasets
+
+[![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)
+[![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. 
+Flower Datasets library supports:
+* **downloading datasets** - choose the dataset from Hugging Face's `datasets`,
+* **partitioning datasets** - customize the partitioning scheme,
+* **creating centralized datasets** - leave parts of the dataset unpartitioned (e.g. for centralized evaluation).
+
+Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets integrates with the following popular formats/frameworks:
+* Hugging Face,
+* PyTorch, 
+* TensorFlow, 
+* Numpy, 
+* Pandas, 
+* Jax,
+* Arrow.
+
+Create **custom partitioning schemes** or choose from the **implemented partitioning schemes**:
+* IID partitioning `IidPartitioner(num_partitions)`
+* more to come in future releases.
+
+# Installation
+
+## With pip
+
+Flower Datasets can be installed from PyPi
+
+```bash
+pip install flwr-datasets
+
+If you plan to change the type of the dataset to run the code with your ML framework, make sure to have it installed too.
+
+# 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(idx, split)` and `load_full(split)`.
+
+Here's a quick example of how to partition the MNIST dataset:
+
+
+`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. 
+
+# 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.
+* Simplification of the dataset transformation to the popular frameworks/types.
+* Creation of the synthetic data,
+* Support for Vertical FL.