Update README.md

README.md

@@ -36,7 +36,7 @@ configs:
       - split: test
         path: datasets/splits/km-random_trainvaltest.csv
 dataset_summary: >- 
-  Dataset containing enzyme kinetic measurements (Kcat, Km, Ki)
+  A large-scale enzyme kinetics database containing experimentally measured kinetic parameters paired with enzyme sequences and substrate SMILES. It's designed for training and benchmarking deep learning models that predict enzyme catalytic activity.
 dataset_description: >- 
   CatPred-DB contains the benchmark datasets introduced alongside the CatPred deep learning framework for predicting in vitro enzyme kinetic parameters. The datasets cover three key kinetic parameters: Kcat, Km, Ki.
 acknowledgements: >- 
@@ -175,25 +175,23 @@ from experimentation to deployment.
 
 ### Install HuggingFace Datasets package
 
-Each subset can be loaded into pytho using the HuggingFace [datasets](https://huggingface.co/docs/datasets/index) library. First, from the command line, install the `datasets` library
+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
 
-```
-$ pip install datasets
+```bash
+pip install datasets
 ```
 
-then, from within python load the datasets library
+### Load a subset
 
-```
->>> import datasets
-```
+```python
+from datasets import load_dataset
 
-### Load model datasets
+# Options: "kcat", "km", "ki"
+ds = load_dataset("kunikohunter/CatPred-DB", "kcat")
 
-To load one of the `CatPred-DB` datasets, use `datasets.load_dataset(...):
-
-```
->>> dataset_tag = ""
->>> dataset_models =
+train = ds["train"]
+val   = ds["validation"]
+test  = ds["test"]
 ```
 
 ---