editing readme

README.md

@@ -8,7 +8,7 @@ tags:
 - metric
 description: Levenshtein (edit) distance
 sdk: gradio
-sdk_version: 5.16.0
+sdk_version: 5.24.0
 app_file: app.py
 pinned: false
 ---
@@ -17,7 +17,7 @@ pinned: false
 
 ## Metric Description
 
-This metric computes the Levenshtein distance, also commonly called "edit distance". The Levenshtein distance measures the number of combined editions, deletions and additions to perform on a string so that it becomes identical to a second one. It is a popular metric for text similarity.
+This metric computes the Levenshtein distance, also commonly called "edit distance". The Levenshtein distance measures the number of combined insertions, deletions and substitutions operations (one per character) to perform on a string so that it becomes identical to a second one. It is a popular metric for text similarity.
 This module directly calls the [Levenshtein package](https://github.com/rapidfuzz/Levenshtein) for fast execution speed.
 
 ## How to Use
@@ -35,6 +35,28 @@ Dictionary mapping to the average Levenshtein distance (lower is better) and the
 
 ### Examples
 
+#### Levenshtein distance
+
+```Python
+import evaluate
+
+levenshtein = evaluate.load("Natooz/Levenshtein")
+results = levenshtein.compute(
+    predictions=[
+        "foo", "baroo"  # 0 and 2 edits
+    ],
+    references=[
+        "foo", "bar"
+    ],
+)
+print(results)
+# {"levenshtein": 1, "levenshtein_ratio": 0.875}
+```
+
+#### Indel (insertion-deletion) distance
+
+The weight of each operation can be provided in order to customize the score. For example, the substitution score can be set to 2 to compute the "indel" distance, so that each substitution is counted as two operations (deletion + insertion).
+
 ```Python
 import evaluate
 
@@ -46,6 +68,7 @@ results = levenshtein.compute(
     references=[
         "foo", "bar"
     ],
+    weights=(1, 1, 2),  # weight of 2 for substitutions
 )
 print(results)
 # {"levenshtein": 1, "levenshtein_ratio": 0.875}