Adaface Sample Natural Language Programming Questions

You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence:
            
            "The bat flew around the room."
            
            You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors.

Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity?
            
            1. Tokenize the sentence and pass it through the pre-trained BERT model.
            2. Extract the embeddings of the word "bat" from the sentence.
            3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words.
            4. Choose the sense with the highest cosine similarity.
            5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words.
            6. Choose the sense with the lowest Euclidean distance.