Update README.md

README.md

@@ -125,7 +125,7 @@ As mentioned in the Data section above, the raw audio data cannot be shared, but
 
 ### Tuning Sensitivity, Specificity, and Indeterminate Fraction
 This module implements a generalization of ROC curve analysis wherein ground truth is binary, but model output can be negative (score below lower threshold), positive (score above upper threshold), or indeterminate (score between thresholds). For the purpose of metric calculations such as sensitivity and specificity, examples marked indeterminate do not count towards either the numerator or denominator. The budget for fraction of examples to be marked indeterminate is configurable as shown below.
-```
+```python
 import numpy as np
 
 from datasets import load_dataset
@@ -173,7 +173,7 @@ sn_eq_sp = issa.sn_eq_sp_graph()
 ### Optimal Tuning for Multi-class Tasks
 The depression and anxiety models were each trained with ordinal regression to predict a scalar score monotonically correlated with the underlying PHQ-9 and GAD-7 questionnaire ground truth sums. As such there are efficient dynamic programming algorithms to select optimal thresholds for multi-class numeric labels under a variety of decision criteria.
 
-```
+```python
 from datasets import load_dataset
 from tuning.optimal_ordinal import MinAbsoluteErrorOrdinalThresholding