Loading fairseq/models/lstm.py +5 −1 Changes for fairseq/models/lstm.py: 5 added lines, 1 removed line. Original line number Diff line number Diff line Loading @@ -80,10 +80,14 @@ class LSTMModel(FairseqModel): utils.print_embed_overlap(embed_dict, dictionary) return utils.load_embedding(embed_dict, dictionary, embed_tokens) pretrained_encoder_embed = None if args.encoder_embed_path: pretrained_encoder_embed = load_pretrained_embedding_from_file( args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim) else: num_embeddings = len(task.source_dictionary) pretrained_encoder_embed = Embedding( num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad() ) if args.share_all_embeddings: # double check all parameters combinations are valid Loading Loading
fairseq/models/lstm.py +5 −1 Changes for fairseq/models/lstm.py: 5 added lines, 1 removed line. Original line number Diff line number Diff line Loading @@ -80,10 +80,14 @@ class LSTMModel(FairseqModel): utils.print_embed_overlap(embed_dict, dictionary) return utils.load_embedding(embed_dict, dictionary, embed_tokens) pretrained_encoder_embed = None if args.encoder_embed_path: pretrained_encoder_embed = load_pretrained_embedding_from_file( args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim) else: num_embeddings = len(task.source_dictionary) pretrained_encoder_embed = Embedding( num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad() ) if args.share_all_embeddings: # double check all parameters combinations are valid Loading