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README.md

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-license: apache-2.0
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+# AnalysisObjectTransformer Model
+
+This repository contains the implementation of the AnalysisObjectTransformer model, a deep learning architecture designed for event classification with reconstructed-object inputs. MultiHeadAttention is used to extract the correlation between jets (hadrons) in the final state. Achieves state-of-the-art performance on final states which can be summarized as jets accompanied by missing transverse energy.
+
+## Model Overview
+
+The AnalysisObjectTransformer model is structured to process jet-level features (energy, mass, area, btag score) in any order (permutation invariance) and event-level features (angle analysis of missing energy and leading jets) to classify signal from background processes to enhance the sensitivity to rare BSM signatures.
+
+### Components
+
+- **Embedding Layers**: Transform input data into a higher-dimensional space for subsequent processing.
+- **Attention Blocks (AttBlock)**: Utilize multi-head attention to capture dependencies between different elements of the input data.
+- **Class Blocks (ClassBlock)**: Extend attention mechanisms to incorporate class tokens, enabling the model to focus on class-relevant features. Implementation based on "Going deeper with transformers": https://arxiv.org/abs/2103.17239
+- **MLP Head**: A sequence of fully connected layers that maps the output of the transformer blocks to the final prediction targets.
+
+## Usage
+
+```python
+from particle_transformer import AnalysisObjectTransformer
+
+model = AnalysisObjectTransformer(input_dim_obj=..., input_dim_event=..., embed_dims=..., linear_dims1=..., linear_dims2=..., mlp_hidden_1=..., mlp_hidden_2=..., num_heads=...)