def predict(self, sentences):

        """

        Method to predict the model on the test dataset. This uses SentEval utils.

        Args:
            sentences(list) : List of sentences.

        Returns
            pd.Dataframe: A pairwise cosine similarity for the sentences provided based on their
            gensen vector representations.

        """

        # self.cache_dir = os.path.join(self.cache_dir, "clean/snli_1.0")
        # self._create_multiseq2seq_model()

        gensen_model = GenSenSingle(
            model_folder=os.path.join(
                self.cache_dir, self.config["data"]["save_dir"]
            ),
            filename_prefix=self.model_name,
            pretrained_emb=self.pretrained_embedding_path,
        )

        reps_h, reps_h_t = gensen_model.get_representation(
            sentences, pool="last", return_numpy=True, tokenize=True
        )

        return compute_correlation_coefficients(reps_h_t)


        Method to predict the model on the test dataset. This uses SentEval utils.

        Args:
            sentences(list) : List of sentences.

        Returns
            pd.Dataframe: A pairwise cosine similarity for the sentences provided based on their
            gensen vector representations.

        
examples/sentence_similarity/gensen_wrapper.py