Update README.md

README.md

@@ -79,20 +79,21 @@ Once a year, the [DCASE community](https://dcase.community/) publishes a [challe
 The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool Renumics Spotlight enables that with just a few lines of code:
 
 Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):
-```jupyterpython
+```python
 !pip install renumics-spotlight datasets[audio]
 ```
 > **_Notice:_**  On Linux, non-Python dependency on libsndfile package must be installed manually. See [Datasets - Installation](https://huggingface.co/docs/datasets/installation#audio) for more information.
 
 Load the dataset from huggingface in your notebook:
-```jupyterpython
+```python
 import datasets
 
 dataset = datasets.load_dataset("renumics/dcase23-task2-enriched", "dev", split="all", streaming=False)
 ```
 
 Start exploring with a simple view that leverages embeddings to identify relevant data segments:
-```jupyterpython
+
+```python
 from renumics import spotlight
 
 df = dataset.to_pandas()
@@ -104,7 +105,7 @@ You can use the UI to interactively configure the view on the data. Depending on
 In this example we focus on the valve class. We specifically look at normal data points that have high anomaly scores in both models. This is one example on how to find difficult example or edge cases:
 
 
-```jupyterpython
+```python
 from renumics import spotlight
 
 extended_layout = datasets.load_dataset_builder("renumics/dcase23-task2-enriched", "dev").config.get_layout(config="extended")
@@ -118,7 +119,7 @@ spotlight.show(df, dtype={'path': spotlight.Audio, "embeddings_ast-finetuned-aud
 When developing your custom model you want to use different kinds of information from you model (e.g. embedding, anomaly scores etc.) to gain further insights into the dataset and the model behvior.
 
 Suppose you have your model's embeddings for each datapoint as a 2D-Numpy array called `embeddings` and your anomaly score as a 1D-Numpy array called `anomaly_scores`. Then you can add this information to the dataset:
-```jupyterpython
+```python
 df['my_model_embedding'] = embeddings
 df['anomaly_score'] = anomaly_scores
 ```
@@ -137,7 +138,7 @@ For more information how to configure the Spotlight UI please refer to the [docu
 For each instance, there is a Audio for the audio, a string for the path, an integer for the section, a string for the d1p (parameter), a string for the d1v (value),
 a ClassLabel for the label and a ClassLabel for the class.
 
-```
+```python
 {'audio': {'array': array([ 0.        ,  0.00024414, -0.00024414, ..., -0.00024414,
          0.        ,  0.        ], dtype=float32),
    'path': 'train/fan_section_01_source_train_normal_0592_f-n_A.wav',