regular sigmoid

setup.py

@@ -3,7 +3,7 @@
 setup(
     name="segment-any-text",
     version="1.0.0",
-    packages=["wtpsplit"],
+    packages=["segment-any-text"],
     description="Universal Robust, Efficient and Adaptable Sentence Segmentation",
     author="Markus Frohmann, Igor Sterner, Benjamin Minixhofer",
     author_email="[email protected]",

wtpsplit/utils.py

@@ -132,31 +132,8 @@ def get_subword_label_dict(label_args, tokenizer):
     return label_dict
 
 
-# numerically more stable sigmoid taken from
-# https://stackoverflow.com/questions/51976461/optimal-way-of-defining-a-numerically-stable-sigmoid-function-for-a-list-in-pyth
-def _positive_sigmoid(x):
-    return 1 / (1 + np.exp(-x))
-
-
-def _negative_sigmoid(x):
-    # Cache exp so you won't have to calculate it twice
-    exp = np.exp(x)
-    return exp / (exp + 1)
-
-
 def sigmoid(x):
-    positive = x >= 0
-    # Boolean array inversion is faster than another comparison
-    negative = ~positive
-
-    # empty contains junk hence will be faster to allocate
-    # Zeros has to zero-out the array after allocation, no need for that
-    # See comment to the answer when it comes to dtype
-    result = np.empty_like(x, dtype=np.float)
-    result[positive] = _positive_sigmoid(x[positive])
-    result[negative] = _negative_sigmoid(x[negative])
-
-    return result
+    return 1 / (1 + np.exp(-x.astype(np.float32)))  # fp32 for better precision
 
 
 def encode(text):