More renaming phone_distance to phone_errors

README.md

@@ -13,7 +13,7 @@ app_file: app.py
 pinned: false
 ---
 
-# Metric Card for Phone Distance
+# Metric Card for Phone Errors
 
 ## Metric Description
 Error rates in terms of distance between articulatory phonological features can help understand differences between strings in the International Phonetic Alphabet (IPA) in a linguistically motivated way. 
@@ -23,8 +23,8 @@ This is useful when evaluating speech recognition or orthographic to IPA convers
 
 ```python
 import evaluate
-phone_distance = evaluate.load("ginic/phone_errors")
-phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
+phone_errors = evaluate.load("ginic/phone_errors")
+phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
 ```
 
 ### Inputs
@@ -51,7 +51,7 @@ The computation returns a dictionary with the following key and values:
 
 Simplest use case to compute phone error rates between two IPA strings: 
 ```python
->>> phone_distance.compute(predictions=["bob", "ði", "spin"], references=["pop", "ðə", "spʰin"])
+>>> phone_errors.compute(predictions=["bob", "ði", "spin"], references=["pop", "ðə", "spʰin"])
 {'phone_error_rates': [0.6666666666666666, 0.5, 0.25], 'mean_phone_error_rate': 0.47222222222222215, 
  'phone_feature_error_rates': [0.08333333333333333, 0.125, 0.041666666666666664], 'mean_phone_feature_error_rate': 0.08333333333333333, 
   'feature_error_rates': [0.027777777777777776, 0.0625, 0.30208333333333337], 'mean_feature_error_rate': 0.13078703703703706}
@@ -59,7 +59,7 @@ Simplest use case to compute phone error rates between two IPA strings:
 
 Normalize phone feature error rate by the length of the reference string: 
 ```python
->>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)
+>>> phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)
 {'phone_error_rates': [0.6666666666666666, 0.5], 'mean_phone_error_rate': 0.5833333333333333, 
  'phone_feature_error_rates': [0.027777777777777776, 0.0625], 'mean_phone_feature_error_rate': 0.04513888888888889, 
  'feature_error_rates': [0.027777777777777776, 0.0625], 'mean_feature_error_rate': 0.04513888888888889}
@@ -67,7 +67,7 @@ Normalize phone feature error rate by the length of the reference string:
 
 Error rates may be greater than 1.0 if the reference string is shorter than the prediction string:
 ```python
->>> phone_distance.compute(predictions=["bob"], references=["po"])
+>>> phone_errors.compute(predictions=["bob"], references=["po"])
 {'phone_error_rates': [1.0], 'mean_phone_error_rate': 1.0, 
  'phone_feature_error_rates': [1.0416666666666667], 'mean_phone_feature_error_rate': 1.0416666666666667, 
  'feature_error_rates': [0.020833333333333332], 'mean_feature_error_rate': 0.020833333333333332}
@@ -75,7 +75,7 @@ Error rates may be greater than 1.0 if the reference string is shorter than the
 
 Empty reference strings will cause an ValueError, you should handle them separately:
 ```python
->>> phone_distance.compute(predictions=["bob"], references=[""])
+>>> phone_errors.compute(predictions=["bob"], references=[""])
 Traceback (most recent call last):
   ...
     raise ValueError("one or more references are empty strings")

phone_errors.py

@@ -71,13 +71,13 @@ Returns:
 
 Examples:
     Compare articulatory differences in voicing in "bob" vs. "pop" and different pronunciations of "the":
->>> phone_distance = evaluate.load("ginic/phone_errors")
->>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
+>>> phone_errors = evaluate.load("ginic/phone_errors")
+>>> phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
 {'phone_error_rates': [0.6666666666666666, 0.5], 'mean_phone_error_rate': 0.5833333333333333, 'phone_feature_error_rates': [0.08333333333333333, 0.125], 'mean_phone_feature_error_rate': 0.10416666666666666, 'feature_error_rates': [0.027777777777777776, 0.0625], 'mean_feature_error_rate': 0.04513888888888889}
 
     Normalize PFER by the length of string with largest number of phones: 
->>> phone_distance = evaluate.load("ginic/phone_errors")
->>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)
+>>> phone_errors = evaluate.load("ginic/phone_errors")
+>>> phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)
 
 """
 
@@ -132,7 +132,7 @@ class PhoneDistance(evaluate.Metric):
                 'references': datasets.Value('string', id="sequence"),
             }),
             # Additional links to the codebase or references
-            codebase_urls=["https://github.com/dmort27/panphon", "https://huggingface.co/spaces/ginic/phone_distance/tree/main"],
+            codebase_urls=["https://github.com/dmort27/panphon", "https://huggingface.co/spaces/ginic/phone_errors/tree/main"],
             reference_urls=["https://pypi.org/project/panphon/", "https://arxiv.org/abs/2308.03917"]
         )