SemanticTextSegmentation NaN With All Stop Words

[haowjy]

When running semantic text segmentation, I found that if the input utterance line is all stop words, (i.e. "Bye. Uh huh. Yeah."), SemanticTextSegmentation._get_similarity fails with ValueError: Input contains NaN.

I found that adding a check for nan in both embeddings could solve this problem.

def _get_similarity(self, text1, text2):
    sentence_1 = [i.text.strip()
                  for i in nlp(text1).sents if len(i.text.split(' ')) > 1]
    sentence_2 = [i.text.strip()
                  for i in nlp(text2).sents if len(i.text.split(' ')) > 2]
    embeding_1 = model.encode(sentence_1)
    embeding_2 = model.encode(sentence_2)
    embeding_1 = np.mean(embeding_1, axis=0).reshape(1, -1)
    embeding_2 = np.mean(embeding_2, axis=0).reshape(1, -1)

    if np.any(np.isnan(embeding_1)) or np.any(np.isnan(embeding_2)):
            return 1

    sim = cosine_similarity(embeding_1, embeding_2)
    return sim

I would like to have someone else look at it because I don't want to make any assumptions that the stop words should be part of the same segments.

[AnjanaRita]

@Haowjy, This solution is looking good to me. Would you like to contribute by creating a PR for this?