Fix pipeline words

pipeline.py

@@ -41,7 +41,7 @@ class PreTrainedPipeline():
         # Postprocess the embeddings to get the most similar words
         similarities = distance.cdist(embeddings.reshape((1,300)), self.comparisons, "cosine")[0]
         top_indices = similarities.argsort()[:10]
-        top_words = [[self.id2h[str(top_indices[i])]] for i in range(10)]
+        top_words = [self.id2h[str(top_indices[i])] for i in range(10)]
         logits = np.exp(-10*np.array(similarities[top_indices]))
         softmax_probs = tf.nn.softmax(logits).numpy()
         top_scores = [round(float(softmax_probs[i]), 3) for i in range(10)]