feat: add readme

README.md

@@ -1,3 +1,115 @@
 # DataSerious
+
 ![Tests](https://github.com/Noza23/dataserious/actions/workflows/tests.yaml/badge.svg)
 [![codecov](https://codecov.io/gh/Noza23/dataserious/graph/badge.svg?token=m9yHQyL0sQ)](https://codecov.io/gh/Noza23/dataserious)
+
+`dataserious` is a Python package that enhances dataclasses with type validation, serialization, and search space generation. It builds on top of the standard `dataclasses` module to provide additional functionality for configuration management, it only has a single dependency on `pyyaml` for YAML support.
+
+## Features
+
+- **Type Validation**: Ensures that the attributes of the dataclass instances match their type annotations.
+- **Serialization**: Supports serialization to and from JSON and YAML formats.
+- **Search Space Generation**: Generates search trees for hyperparameter tuning in grid and random search.
+
+## Installation
+
+You can install `dataserious` with YAML support using pip.
+
+```sh
+pip install "dataserious[yaml] @ git+https://github.com/Noza23/dataserious.git"
+```
+## Usage
+
+### Defining a Configuration Class
+
+To define a configuration class, inherit from `BaseConfig` and use `ConfigField` for fields that require additional metadata.
+
+```python
+from dataserious import BaseConfig, ConfigField
+
+class ModelConfig(BaseConfig):
+    name: str
+    """Name of the model."""
+    n_layers: int = ConfigField(searchable=True, description="Number of layers in the model.")
+    n_heads: int = ConfigField(searchable=True)
+    """Number of heads in the model."""
+```
+
+### Creating Template Configurations
+```python
+ModelConfig.to_schema()
+ModelConfig.schema_to_yaml("schema.yaml")
+```
+```yaml
+name: 'str: Name of the model.'
+n_layers: 'int: Number of layers in the model.'
+n_heads: 'int: Number of heads in the model.'
+```
+
+### Loading and Saving Configurations
+
+You can load and save configurations in JSON and YAML formats.
+
+```python
+config = ModelConfig(name="GPT", n_layers=12, n_heads=12)
+config.to_yaml("config.yaml")
+loaded_config = ModelConfig.from_yaml("config.yaml")
+```
+
+### Generating Search Trees
+
+Generate search trees for hyperparameter tuning.
+
+```python
+print(config.to_search_tree())
+config.search_tree_to_yaml("search_tree.yaml")
+```
+
+```yaml
+n_layers:
+- int
+n_heads:
+- int
+```
+
+### Serving Grid and Random Search Spaces
+
+1. Filled out search tree *test.yaml*:
+    ```yaml
+    n_layers:
+    - 12
+    - 24
+    - 36
+    n_heads:
+    - 12
+    - 24
+    - 36
+    ```
+2. Loading the search tree and generating search spaces:
+    ```python
+    configs = config.get_configs_grid_from_path("test.yaml")
+    configs_random = config.get_configs_random_from_path("test.yaml", n=2, seed=42)
+    ```
+
+3. Resulting Configs:
+    ```python
+    [print(json.dumps(config.to_dict(), indent=4)) for config in configs]
+    print("\nRandom configs:")
+    [print(json.dumps(config.to_dict(), indent=4)) for config in configs_random]
+    ```
+
+
+### Validating Configurations
+
+Ensure that the configurations match the expected types.
+
+```python
+try:
+    config = ModelConfig(name="GPT", n_layers="twelve", n_heads=12)
+except TypeError as e:
+    print(e)
+```
+
+## Contributing
+
+Contributions are welcome!

dataserious/__init__.py

@@ -1,5 +1,6 @@
 """Python dataclasses enhanced with type validation and serialization."""
 
 from .base import BaseConfig
+from .fields import ConfigField
 
-__all__ = ["BaseConfig"]
+__all__ = ["BaseConfig", "ConfigField"]