Add visualize_preds method for PAWS evaluation

import copy

import spacy

class SBERT_Model:

    def __init__(self, name, filepath, dataset, probing=False):

        """

        print(f"Plot saved to {plot_path}. Time it took to generate: {t2-t1:.2f} seconds")

        print(f'{legend_path} stores the information about the data points on the plot.')

    def visualize_preds(self):

        print(f"Average cosine similarity: {np.average(self.preds)}; Standard deviation: {np.std(self.preds)}")

        fig, ax = plt.subplots(1, 1, figsize=(8, 5), tight_layout=True)

        ax.hist(self.preds, bins=20)

        plt.xlim([0, 1])

        ax.set_title(

            f"{self.name} - histogram " + r"$\mu$" + f"={round(np.average(self.preds), 3)}, " + r"$\sigma$" f"={round(np.std(self.preds), 3)}")

        plt.ylabel('Frequency')

        plt.xlabel('Cosine similarity between sentence pair')

        plt.savefig(f'{self.name}_histogram.png', bbox_inches='tight')

    def get_word_importance(self,sentence_pair, mask="[MASK]"):

        nlp = spacy.load("en_core_web_sm")

        sentence1 = nlp(sentence_pair[0])

        """

        preds = []

        for i in range(len(self.cosine_scores[0])):

            preds.append(self.cosine_scores[i][i])

            preds.append(float(self.cosine_scores[i][i]))

        return preds

    def get_cosine_scores(self, emb1, emb2):