Update space name

README.md

@@ -41,7 +41,7 @@ pip install vendi_score[all]
 ```
 To calculate the score, pass a list of samples and a similarity function or a string identifying a predefined class of similarity functions (see below).
 ```
->>> vendiscore = evaluate.load("danf0/vendiscore")
+>>> vendiscore = evaluate.load("Vertaix/vendiscore")
 >>> samples = ["Look, Jane.",
                "See Spot.",
                "See Spot run.",
@@ -86,7 +86,7 @@ Given n samples, the value of the Vendi Score ranges between 1 and n, with highe
 
 ```
 >>> import numpy as np
->>> vendiscore = evaluate.load("danf0/vendiscore", "int")
+>>> vendiscore = evaluate.load("Vertaix/vendiscore", "int")
 >>> samples = [0, 0, 10, 10, 20, 20]
 >>> k = lambda a, b: np.exp(-np.abs(a - b))
 >>> vendiscore.compute(samples=samples, k=k)
@@ -95,7 +95,7 @@ Given n samples, the value of the Vendi Score ranges between 1 and n, with highe
 
 If you already have precomputed a similarity matrix:
 ```
->>> vendiscore = evaluate.load("danf0/vendiscore", "K")
+>>> vendiscore = evaluate.load("Vertaix/vendiscore", "K")
 >>> K = np.array([[1.0, 0.9, 0.0],
                   [0.9, 1.0, 0.0],
                   [0.0, 0.0, 1.0]])
@@ -108,7 +108,7 @@ If your similarity function is a dot product between `n` normalized
 to compute the Vendi Score using the covariance matrix, `X @ X.T`.
 (If the rows of `X` are not normalized, set `normalize = True`.)
 ```
->>> vendiscore = evaluate.load("danf0/vendiscore", "X")
+>>> vendiscore = evaluate.load("Vertaix/vendiscore", "X")
 >>> X = np.array([[100, 0], [99, 1], [1, 99], [0, 100]])
 >>> vendiscore.compute(samples=X, score_dual=True, normalize=True)
 {'VS': 1.99989...}
@@ -116,7 +116,7 @@ to compute the Vendi Score using the covariance matrix, `X @ X.T`.
 
 Text similarity can be calculated using n-gram overlap or using inner products between embeddings from a neural network.
 ```
->>> vendiscore = evaluate.load("danf0/vendiscore", "text")
+>>> vendiscore = evaluate.load("Vertaix/vendiscore", "text")
 >>> sents = ["Look, Jane.", "See Spot.", "See Spot run.", "Run, Spot, run.", "Jane sees Spot run."]
 >>> ngram_vs = vendiscore.compute(samples=sents, k="ngram_overlap", ns=[1, 2])["VS"]
 >>> bert_vs = vendiscore.compute(samples=sents, k="text_embeddings", model_path="bert-base-uncased")["VS"]

vendiscore.py

@@ -53,7 +53,7 @@ Args:
 Returns:
     VS: The Vendi Score. 
 Examples:
-    >>> vendiscore = evaluate.load("danf0/vendiscore", "text")
+    >>> vendiscore = evaluate.load("Vertaix/vendiscore", "text")
     >>> samples = ["Look, Jane.",
                    "See Spot.",
                    "See Spot run.",