03b0e7061cf477fbeccb9c128ee76603df582d86,scripts/word_embeddings/evaluate_pretrained.py,,load_embedding_from_path,#Any#,127

Before Change

                    args.embedding_path)

        // Add OOV words if the token_embedding can impute them
        token_set = set()
        token_set.update(
            filter(lambda x: x in model,
                   evaluation.get_tokens_in_evaluation_datasets(args)))

After Change

            // Pre-compute all words in vocabulary in case of analogy evaluation
            idx_to_token = [
                model.token_to_idx[idx]
                for idx in range(len(model.token_to_idx))
            ]
            if args.max_vocab_size:
                idx_to_token = idx_to_token[:args.max_vocab_size]
        else:

Instances