151f9e7d141372ce45fc4367b81385eeccc95093,sequenceLabelling/preprocess.py,,to_vector_simple_with_elmo,#Any#Any#Any#Any#Any#,256

Before Change

    the ELMo contextualized embeddings, introducing <PAD> and <UNK> 
    padding token vector when appropriate
    
    window = tokens[-maxlen:]
    
    // TBD: use better initializers (uniform, etc.) 
    x = np.zeros((maxlen, embeddings.embed_size), )

    if lowercase:
        x_elmo = embeddings.get_sentence_wector_ELMo(lower(tokens))
    else:
        x_elmo = embeddings.get_sentence_wector_ELMo(tokens)

    // TBD: padding should be left and which vector do we use for padding? 
    // and what about masking padding later for RNN?
    for i, word in enumerate(window):
        if lowercase:
            word = _lower(word)
        if num_norm:
            word = _normalize_num(word)
        x[i,:] = np.concat(
                embeddings.get_word_vector(word).astype("float32"),
                x_elmo[i]
                )

    return x

def to_casing_single(tokens, maxlen=300):
    

After Change

    padding token vector when appropriate
    
    subtokens = []
    for i in range(0, len(tokens)):
        local_tokens = []
        for j in range(0, min(len(tokens[i]), maxlen)):
            if lowercase:
                local_tokens.append(_lower(tokens[i][j]))
            else:
                local_tokens.append(tokens[i][j])
        subtokens.append(local_tokens)
    return embeddings.get_sentence_vector_with_ELMo(subtokens)


def to_casing_single(tokens, maxlen=300):

Instances