Update README.md

README.md

@@ -158,16 +158,6 @@ Each split file is named `{parameter}-{strategy}_{subset}.csv`. The subsets are:
 | `trainval` | Train + val combined | Retrain final model after hyperparameters are locked in |
 | `trainvaltest` | All data combined | Train a release model once all evaluation is complete |
 
-### Recommended Workflow
-```
-train + val         →   tune architecture and hyperparameters
-trainval + test     →   final benchmark (report results here)
-trainvaltest        →   train the final released model on all available data
-```
-
-This three-stage approach is standard practice in ML: you only touch the test set once,
-and the combined files make it easy to retrain on progressively more data as you move
-from experimentation to deployment.
 
 ---
 
@@ -178,20 +168,20 @@ from experimentation to deployment.
 Each subset can be loaded into python using the HuggingFace [datasets](https://huggingface.co/docs/datasets/index) library. First, from the command line, install the `datasets` library
 
 ```bash
-pip install datasets
+>>> pip install datasets
 ```
 
 ### Load a subset
 
 ```python
->>>from datasets import load_dataset
+>>> from datasets import load_dataset
 
 # Options: "kcat", "km", "ki"
->>>ds = load_dataset("kunikohunter/CatPred-DB", "kcat")
+>>> ds = load_dataset("kunikohunter/CatPred-DB", "kcat")
 
->>>train = ds["train"]
->>>val   = ds["validation"]
->>>test  = ds["test"]
+>>> train = ds["train"]
+>>> val   = ds["validation"]
+>>> test  = ds["test"]
 ```
 
 ```
@@ -203,6 +193,43 @@ Generating validation split: 100%|███████████████
 Generating test split: 100%|██████████████████████████████████████████████████████████████████████████████████| 23197/23197 [00:00<00:00, 74253.91 examples/s]
 ```
 
+### Key columns
+
+| Column | Description |
+| --- | --- |
+| `sequence` | Enzyme amino acid sequence |
+| `uniprot` | UniProt identifier |
+| `reactant_smiles` | Substrate SMILES |
+| `value` | Raw kinetic value |
+| `log10_value` | Log₁₀-transformed value — **use this as your target** |
+| `temperature` | Assay temperature (°C), nullable |
+| `ph` | Assay pH, nullable |
+| `ec` | EC number |
+| `sequence_40cluster` | Cluster ID at 40% identity — use for similarity-based splits |
+
+### Recommended split workflow
+
+```
+train + val         →   tune architecture and hyperparameters
+trainval + test     →   final benchmark (report results here)
+trainvaltest        →   train the final released model on all available data
+```
+
+This three-stage approach is standard practice in ML: you only touch the test set once,
+and the combined files make it easy to retrain on progressively more data as you move
+from experimentation to deployment.
+
+### Basic training setup
+```python
+>>> df = ds["train"].to_pandas()
+
+>>> X_seq = df["sequence"]
+>>> X_sub = df["reactant_smiles"]
+>>> y     = df["log10_value"]
+
+# Drop rows with missing targets or substrates
+>>> mask = y.notna() & X_sub.notna()
+>>> df   = df[mask]
 ---
 
 ## Citation