Update README.md

README.md

@@ -76,4 +76,47 @@ train-eval-index:
     train/eval: train/eval
 size_categories:
 - 100K<n<1M
----
+---
+
+## Dataset Description
+
+The **Particle Physics Lagrangian Dataset** was created to train a BART model for generating Lagrangians from particle fields and their symmetries. This task supports research in field theories within particle physics.
+
+### Data Generation
+
+The dataset is generated through a pipeline utilizing AutoEFT, which helps automate the creation of effective field theories (EFTs). This tool is crucial for creating invariant terms based on specified fields and symmetries.
+
+### Dataset Sampling
+
+Due to the vast space of possible Lagrangians, careful sampling is essential:
+
+1. **Uniform Dataset**: Provides evenly distributed Lagrangians for validation.
+2. **Sampled Dataset**: Focuses on extreme cases to optimize learning, based on insights from natural language processing.
+
+#### Key Features
+
+- **Field Count**: Skews towards simpler Lagrangians with fewer fields.
+- **Spin Types**: Includes a balanced mix of scalars and fermions.
+- **Gauge Groups**: Uses SU(3), SU(2), and U(1) representations.
+- **Trilinear Interaction Enrichment**: Includes crucial interaction terms fundamental to particle physics.
+
+### Data Fields
+
+- **fields**: List of input fields identified by their quantum numbers.
+- **Lagrangian**: The corresponding Lagrangian for the input fields.
+- **train/eval**: A flag describing whether the datapoint was used for training or evaluation.
+
+### Encoding scheme
+
+To facilitate understanding by the transformer model, the dataset undergoes a custom tokenization process that preserves the essential information of fields and Lagrangians:
+
+- Fields and derivatives are tokenized to encapsulate quantum numbers, spins, and gauge symmetries.
+- Key interactions are represented through positional tokens.
+- Tokenization ensures all necessary contraction and symmetry details are conveyed.
+
+For further details on the methods and theoretical underpinnings of this work, please refer to the paper "Generating Particle Physics Lagrangians with Transformers" [arXiv:xxxx.xxxxx](https://arxiv.org/abs/xxxx.xxxxx). 
+
+
+### Usage
+
+Ideal for sequence-to-sequence tasks, this dataset is optimized for training transformer models to derive particle physics Lagrangians efficiently.