def parse_embedding(embed_path):
    """Parse embedding text file into a dictionary of word and embedding tensors.

    The first line can have vocabulary size and dimension. The following lines
    should contain word and embedding separated by spaces.

    Example:
        2 5
        the -0.0230 -0.0264  0.0287  0.0171  0.1403
        at -0.0395 -0.1286  0.0275  0.0254 -0.0932
    """
    embed_dict = {}
    with open(embed_path) as f_embed:
        next(f_embed)  # skip header
        for line in f_embed:
            pieces = line.rstrip().split(" ")
            embed_dict[pieces[0]] = torch.Tensor(
                [float(weight) for weight in pieces[1:]]
            )
    return embed_dict
Parse embedding text file into a dictionary of word and embedding tensors.

    The first line can have vocabulary size and dimension. The following lines
    should contain word and embedding separated by spaces.

    Example:
        2 5
        the -0.0230 -0.0264  0.0287  0.0171  0.1403
        at -0.0395 -0.1286  0.0275  0.0254 -0.0932
    
structured_prompting/fairseq-version/fairseq/fairseq/utils.py