docs: Polish and improve README

README.md

@@ -1,6 +1,11 @@
 # DocILE: Document Information Localization and Extraction Benchmark
-[![Tests](https://github.com/rossumai/docile/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/rossumai/docile/actions/workflows/tests.yml)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+<p align="center">
+  <a href="https://github.com/rossumai/docile/actions/workflows/tests.yml"><img src="https://github.com/rossumai/docile/actions/workflows/tests.yml/badge.svg?branch=main" alt="Tests"></a>
+  <a href="https://pypi.org/project/docile-benchmark/"><img src="https://img.shields.io/pypi/v/docile-benchmark" alt="PyPI version"></a>
+  <a href="https://pypi.org/project/docile-benchmark/"><img src="https://img.shields.io/pypi/pyversions/docile-benchmark" alt="Supported Python versions"></a>
+  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
+</p>
 
 Repository to work with the [DocILE dataset and benchmark](https://docile.rossum.ai/), used in the DocILE'23 CLEF Lab and ICDAR Competition.
 The competition deadline is on ~~May 10~~ **May 24, 2023** and comes with a **$9000 prize pool**.
@@ -38,10 +43,14 @@ You can also work with the zipped datasets when you turn off image caching (chec
 
 ## Installation
 
-### Option 1: Install as a library
+Choose one of the two options below: install `docile` as a library, or use docker.
+
+<details>
+<summary><b>Option 1: Install as a library</b></summary>
+
 Install the library with:
 ```shell
-pip install docile-benchmark
+uv pip install docile-benchmark
 ```
 
 To convert pdfs into images, the library uses https://github.com/Belval/pdf2image. On linux you might need to install:
@@ -56,13 +65,17 @@ brew install poppler
 
 Now you have all dependencies to work with the dataset annotations, pdfs, pre-comptued OCR and to run the evaluation. You can install extra dependencies by running the following (although using one of the provided dockerfiles, as explained below, might be easier in this case):
 ```shell
-pip install "docile-benchmark[interactive]"
-pip install "docile-benchmark[ocr]"
+uv pip install "docile-benchmark[interactive]"
+uv pip install "docile-benchmark[ocr]"
 ```
 
 The first line installs additional dependencies allowing you to use the interactive dataset browser in [docile/tools/dataset_browser.py](docile/tools/dataset_browser.py) and the [tutorials](tutorials/). The second line let's you rerun the OCR predictions from scratch (e.g., if you'd like to run it with different parameters) but to make it work, you might need additional dependencies on your system. Check https://github.com/mindee/doctr for the installation instructions (for pytorch).
 
-### Option 2: Use docker
+</details>
+
+<details>
+<summary><b>Option 2: Use docker</b></summary>
+
 There are two Dockerfiles available with the dependencies preinstalled:
 
 * `Dockerfile` is a lighter, CPU-only, version with all necessary dependencies to use the dataset with the pre-computed OCR and interactive browser.
@@ -83,6 +96,8 @@ docker compose exec jupyter bash
 ```
 After that the `docile` library and its dependencies are already installed in the environment; prefix commands with `uv run ...` (e.g. `uv run jupyter lab`) to run them inside it.
 
+</details>
+
 ## Predictions format and running evaluation
 
 To evaluate predictions for tasks KILE or LIR on the test set, you have to make a submission to the benchmark on the [Robust Reading Competition](https://rrc.cvc.uab.es/?ch=26) website. To evaluate on the validation set, use the following command: