Addressed reviewers comments

.gitignore

@@ -164,3 +164,4 @@ cython_debug/
 
 # model output directories
 outputs/
+examples/*.xyz

README.rst

@@ -61,10 +61,10 @@ Having problems or ideas?
 -------------------------
 
 Having a problem with metatensor-models? Please let us know by `submitting an issue
-<https://github.com/ceriottm/MeshLODE/issues>`_.
+<https://github.com/metatensor-models/issues>`_.
 
 Submit new features or bug fixes through a `pull request
-<https://github.com/ceriottm/MeshLODE/pulls>`_.
+<https://github.com/metatensor-models/pulls>`_.
 
 .. marker-contributing
 
@@ -73,7 +73,7 @@ Contributors
 
 Thanks goes to all people that make metatensor-models possible:
 
-.. image:: https://contrib.rocks/image?repo=ceriottm/MeshLODE
+.. image:: https://contrib.rocks/image?repo=metatensor-models
    :target: https://github.com/lab-cosmo/metatensor-models/graphs/contributors
 
 .. |tests| image:: https://github.com/lab-cosmo/metatensor-models/workflows/Tests/badge.svg

docs/src/getting-started/index.rst

@@ -1,7 +1,7 @@
 Getting started
 ===============
 
-This sections describes, how to install the package, and its most basic commands.
+This sections describes how to install the package, and its most basic commands.
 
 .. toctree::
    :maxdepth: 1

docs/src/getting-started/usage.rst

@@ -6,7 +6,7 @@ general help of `metatensor-models` can be accessed using
 
 .. code-block:: bash
 
-    metatensor-models -h
+    metatensor-models --help
 
 We now demonstrate how to `train` and `evaluate` a model from the command line. For this
 example we use the :ref:`architecture-soap-bpnn` architecture and a subset of the `QM9
@@ -16,11 +16,14 @@ from our :download:`website <../../static/qm9_reduced_100.xyz>`.
 Training
 ########
 
-For performing a model training in `metatensor-models` uses the hydra framework. Hydra
-allows to dynamical composition and override of config files and the command line and
-has powerful tools to create multiple training runs with a single command. We will not
-explain here how to use hydra in detail, as we only use a few functions ins this example
-but rather refer to their good package documentation.
+To train models, `metatensor-models` uses the hydra framework. Hydra is a framework
+developed by Facebook AI for elegantly configuring complex applications. It's primarily
+used for managing command-line arguments in Python applications, allowing for a
+structured and dynamic approach to configuration. It allows to dynamical composition and
+override of config files and the command line and has powerful tools to create multiple
+training runs with a single command. We will not explain here how to use hydra in
+detail, as we only use a few functions ins this example but rather refer to their good
+package documentation.
 
 The sub-command to start a model training is
 
@@ -47,8 +50,9 @@ created. By default, this output directory is used to store Hydra output for the
 behavior in the parameter file. To start the training using the ``parameters.yaml`` in
 the current directory type.
 
-.. literalinclude:: ../../../examples/train.sh
+.. literalinclude:: ../../../examples/usage.sh
     :language: bash
+    :lines: 3-8
 
 
 Evaluation
@@ -60,5 +64,6 @@ The sub-command to evaluate a already trained model is
 
     metatensor-models eval
 
-.. literalinclude:: ../../../examples/eval.sh
+.. literalinclude:: ../../../examples/usage.sh
     :language: bash
+    :lines: 9-

docs/static/parameters.yaml

@@ -1,18 +1,9 @@
 defaults:
-  - architecture: soap_bpnn  # the architecture we will use to train a model
+  - _self_  # mandatory parameter to avoid hydra warnings
+  - architecture: soap_bpnn  # architecture used to train the model
 
-architecture:
-  # Optional section to override the default parameters of the architecture
-  soap:
-    cutoff: 7.5
-
-  # Optional section to override the default parameters of the trainer
-  training:
-    batch_size: 8
-    num_epochs: 1
-
-# Positional sectopn defining the parameters for structure and target data
+# Section defining the parameters for structure and target data
 dataset:
   structure_path: "qm9_reduced_100.xyz" # file where the positions are stored
   targets_path: "qm9_reduced_100.xyz" # file with target values (i.e energies)
-  target_value: "U0" # the name of the target value in `targets_path` used for training
+  target_value: "U0" # name of the target value in `targets_path`