Update README.md

README.md

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-tRNA based classification model and generic files as learning dataset and supplementary data used for labeling + HTML file with step by step research description
+# tRNA-based classification model
+
+The dataset contains:
+1. Generic files used for training the dataset  
+2. Supplementary data used for labeling  
+3. An HTML file with a step-by-step description of the research  
+4. Python scripts used to train the models  
+
+## Setup
+Download Miniconda and use:
+```bash
+conda env create -f environment.yml
+```
+to replicate the working environment.
+
+If any packages are missing during python code execution, install them manually using pip, based on import error messages.
+
+## Steps for replication:
+1. Download supplementary data from https://doi.org/10.7554/eLife.71402
+2. **ftp_urls.txt** contains a list of genome download addresses (most of them are available).
+3. Run **full.sh** to download genomes and extract features for model training from full dataset, saved as **FEATURES_ALL.ndjson** (genomes are removed to preserve memory)
+4. Run **80_20_split_fixed.py** on **FEATURES_ALL.ndjson** together with both supplementary files to perform an automatic stratified 80/20 split, with archaeal and contaminated genomes filtered out.
+5. Run **Mass_models.py** on **FEATURES_ALL.ndjson**, **Supp1.csv**, **Supp2.xlsx**
+
+Run Mass_models.py using FEATURES_ALL.ndjson, Supp1.csv, and Supp2.xlsx.
+Example run settings:
+```python
+python3 Mass_models.py   
+  --ndjson split_dataset/subset01/   
+  --supp2 Supp2.xlsx   
+  --supp1 Supp1.csv   
+  --outdir .   
+  --train_mode both   
+  --weight_mode both   
+  --model all   
+  --metric all   
+  --n_trials 30   
+  --timeout 5400
+```
+
+
+![Models benchmark](https://cdn-uploads.huggingface.co/production/uploads/6605ab970fabda441257b27e/Wt-I28Er9KGwEJOSOzSLu.png)