Update README.md

README.md

@@ -94,22 +94,6 @@ The EveNet paper evaluates the pretrained model on four downstream tasks:
 3. **Quantum correlations in (t\bar{t}) dilepton events:** EveNet pretrained on 500 million events achieved a normalised uncertainty (\Delta D) of **1.61 %** on the entanglement‑sensitive observable after fine‑tuning with 15 % of typical training statistics, outperforming scratch and self‑supervised baselines. It also reached **82 % pairing accuracy**, several points above scratch (80 %) and SSL (79 %) models.
 4. **Anomaly detection on CMS Open Data:** Generative diffusion heads were fine‑tuned on 2016 dimuon data to rediscover the Υ meson. EveNet replaced the conditional normalising flow baseline with a generative model that directly produces dimuon point clouds; after calibration, it achieved competitive or superior anomaly significance while maintaining physical fidelity.
 
-## How to Get Started
-
-Install the package via pip:
-
-```bash
-pip install evenet
-
-# convert .npz files to parquet and prepare normalization
-python preprocessing/preprocess.py --config share/event_info/pretrain.yaml --file /path/to/mydata.npz --store_dir /path/to/output
-
-# fine‑tune the model
-evenet-train my_finetuning_config.yaml
-```
-
-Pretrained weights can be loaded by specifying `pretrain_model_load_path` in the YAML configuration. For a detailed description of configuration options, consult the documentation site.
-
 ## Citation
 
 If you use this model in your research, please cite: