Update the examples.

README.md

@@ -44,7 +44,7 @@ To calculate the score, pass a list of samples and a similarity function or a st
                "See Spot run.",
                "Run, Spot, run.",
 	       "Jane sees Spot run."]
->>> results = vendiscore.compute(samples, k="ngram_overlap", ns=[1, 2])
+>>> results = vendiscore.compute(samples=samples, k="ngram_overlap", ns=[1, 2])
 >>> print(results)
 {'VS': 3.90657...}
 ```
@@ -91,7 +91,7 @@ Given n samples, the value of the Vendi Score ranges between 1 and n, with highe
 >>> vendiscore = evaluate.load("danf0/vendiscore")
 >>> samples = [0, 0, 10, 10, 20, 20]
 >>> k = lambda a, b: np.exp(-np.abs(a - b))
->>> vendiscore.compute(samples, k)
+>>> vendiscore.compute(samples=samples, k=k)
 2.9999
 ```
 
@@ -100,7 +100,7 @@ If you already have precomputed a similarity matrix:
 >>> K = np.array([[1.0, 0.9, 0.0],
                   [0.9, 1.0, 0.0],
                   [0.0, 0.0, 1.0]])
->>> vendiscore.compute(K, score_K=True)
+>>> vendiscore.compute(samples=K, score_K=True)
 2.1573
 ```
 
@@ -110,7 +110,7 @@ to compute the Vendi Score using the covariance matrix, `X @ X.T`.
 (If the rows of `X` are not normalized, set `normalize = True`.)
 ```
 >>> X = np.array([[100, 0], [99, 1], [1, 99], [0, 100])
->>> vendiscore.compute(X, score_dual=True, normalize=True)
+>>> vendiscore.compute(samples=X, score_dual=True, normalize=True)
 1.9989...
 ```
 
@@ -120,8 +120,8 @@ The default embeddings are from the pool-2048 layer of the torchvision version o
 >>> from torchvision import datasets
 >>> mnist = datasets.MNIST("data/mnist", train=False, download=True)
 >>> digits = [[x for x, y in mnist if y == c] for c in range(10)]
->>> pixel_vs = [vendiscore.compute(imgs, k="pixels") for imgs in digits]
->>> inception_vs = [vendiscore.compute(imgs, k="image_embeddings", batch_size=64, device="cuda") for imgs in digits]
+>>> pixel_vs = [vendiscore.compute(samples=imgs, k="pixels") for imgs in digits]
+>>> inception_vs = [vendiscore.compute(samples=imgs, k="image_embeddings", batch_size=64, device="cuda") for imgs in digits]
 >>> for y, (pvs, ivs) in enumerate(zip(pixel_vs, inception_vs)): print(f"{y}\t{pvs:.02f}\t{ivs:02f}")
 0       7.68    3.45
 1       5.31    3.50
@@ -142,9 +142,9 @@ Text similarity can be calculated using n-gram overlap or using inner products b
              "See Spot run.",
              "Run, Spot, run.",
 	     "Jane sees Spot run."]
->>> ngram_vs = vendiscore.compute(sents, k="ngram_overlap", ns=[1, 2])
->>> bert_vs = vendiscore.compute(sents, k="text_embeddings", model_path="bert-base-uncased")
->>> simcse_vs = vendiscore.compute(sents, k="text_embeddings", model_path="princeton-nlp/unsup-simcse-bert-base-uncased")
+>>> ngram_vs = vendiscore.compute(samples=sents, k="ngram_overlap", ns=[1, 2])
+>>> bert_vs = vendiscore.compute(samples=sents, k="text_embeddings", model_path="bert-base-uncased")
+>>> simcse_vs = vendiscore.compute(samples=sents, k="text_embeddings", model_path="princeton-nlp/unsup-simcse-bert-base-uncased")
 >>> print(f"N-grams: {ngram_vs:.02f}, BERT: {bert_vs:.02f}, SimCSE: {simcse_vs:.02f})
 N-grams: 3.91, BERT: 1.21, SimCSE: 2.81
 ```