added some docs to readme

README.md

@@ -17,17 +17,43 @@ pinned: false
 ***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
 
 ## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
+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.
 
 ## How to Use
-*Give general statement of how to use the metric*
+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"]
+logmetric = evaluate.load("svenwey/logscoremetric")
+>>> results = logmetric.compute(predictions=predictions, 
+...                             references=references)
+>>> print(results["timestamp_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"]
+logmetric = evaluate.load("svenwey/logscoremetric")
+>>> results = logmetric.compute(predictions=predictions, 
+...                             references=references)
+>>> print(results["timestamp_score"])
+0.0
+```
 
-*Provide simplest possible example for using the metric*
 
 ### Inputs
 *List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
-
+- **predictions** *(string list): The logs, as predicted/generated by the ML model. **Important: Every logfile is only one string, even if it contains multiple lines!***
+- **references** *(string list): The reference logs (ground truth)*
 ### Output Values
 
 *Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*