reflect updates in readme

README.md

@@ -19,33 +19,30 @@ pinned: false
 ## Metric Description
 This metric is used for evaluating how good a generated log(file) is, given a reference. 
 
-The metric measures two different aspects
-
-1. It evaluates if the predicted log has the correct amount of timestamps, if timestamps are monotonically increasing and if the timestamps are consistent in their format.
-2. For measuring the similarity in content (without timestamps), this metric uses sacrebleu.
+The metric evaluates if the predicted log has the correct amount of timestamps, if timestamps are monotonically increasing and if the timestamps are consistent in their format.
 
 ## How to Use
 The metric can be just by simply giving the predicted log and the reference log as string.
 
 Example with timestamps that are of correct amount, consistent, monotonically increasing (-> timestamp score of 1.0):
 ```
->>> predictions = ["2024-01-12 11:23 hello, nice to meet you \n 2024-01-12 11:24 So we see each other again"]
->>> references = ["2024-02-14 This is a hello to you \n 2024-02-15 Another hello"]
+>>> predictions = ["2024-01-12 11:23 It's over Anikin, I have the high ground \n 2024-01-12 11:24 You underestimate my power!"]
+>>> references = ["2024-02-14 Hello there! \n 2024-02-14 General Kenobi! You're a bold one, aren't you?"]
 logmetric = evaluate.load("svenwey/logmetric")
 >>> results = logmetric.compute(predictions=predictions, 
 ...                             references=references)
->>> print(results["timestamp_score"])
+>>> print(results["score"])
 1.0
 ```
 
 Example with timestamp missing from prediction:
 ```
->>> predictions = ["hello, nice to meet you"]
->>> references = ["2024-02-14 This is a hello to you"]
+>>> predictions = ["You were my brother Anikin"]
+>>> references = ["2024-01-12 You were my brother Anikin"]
 logmetric = evaluate.load("svenwey/logmetric")
 >>> results = logmetric.compute(predictions=predictions, 
 ...                             references=references)
->>> print(results["timestamp_score"])
+>>> print(results["score"])
 0.0
 ```