Update README.md

README.md

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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.
+This repository contains the implementation of the AnalysisObjectTransformer model, a deep learning architecture designed for event classification with data from the CERN LHC.
+The model operates reconstructed-object and event-level features. MultiHeadAttention is used to extract the correlation between reconstructed objects such as jets (hadrons) or leptons in the final state, while event-level features capture the event summary, such as total hadronic energy or missing transverse energy.
+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.
+The AnalysisObjectTransformer model is structured to process object-level features, in the case of jets: energy, mass, area, btag score, in any order (permutation invariance) and event-level features (HT, MET) to classify signal from background processes to enhance the sensitivity to rare BSM signatures.
 
 ### Components
 
+
+See [here](https://excalidraw.com/#json=tCXGu1s6Az9wh4md45JU6,A3ezTIoqB10HVxOt4hhRSA) for complete architecure:
+
 - **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
 
 Firstly, clone the repository: