build_data.py
def main():
    """Procedure to build data

    You MUST RUN this procedure. It iterates over the whole dataset (train,
    dev and test) and extract the vocabularies in terms of words, tags, and
    characters. Having built the vocabularies it writes them in a file. The
    writing of vocabulary in a file assigns an id (the line #) to each word.
    It then extract the relevant GloVe vectors and stores them in a np array
    such that the i-th entry corresponds to the i-th word in the vocabulary.


    Args:
        config: (instance of Config) has attributes like hyper-params...

    """
    # get config and processing of words
    config = Config(load=False)
    processing_word = get_processing_word(lowercase=True)

    # Generators
    dev   = CoNLLDataset(config.filename_dev, processing_word)
    test  = CoNLLDataset(config.filename_test, processing_word)
    train = CoNLLDataset(config.filename_train, processing_word)

    # Build Word and Tag vocab
    vocab_words, vocab_tags = get_vocabs([train, dev, test])
    vocab_word2vec = get_word2vec_vocab(config.filename_word2vec)

    vocab = vocab_words & vocab_word2vec
    vocab.add(str(UNK.encode('utf-8')))
    vocab.add(str(NUM.encode('utf-8')))

    # Save vocab
    write_vocab(vocab, config.filename_words)
    write_vocab(vocab_tags, config.filename_tags)

    # Trim GloVe Vectors
    vocab = load_vocab(config.filename_words)
    export_trimmed_word2vec_vectors(vocab, config.filename_word2vec,
                                config.filename__word2vec_trimmed, config.dim_word)

    # Build and save char vocab
    train = CoNLLDataset(config.filename_train)
    vocab_chars = get_char_vocab(train)
    write_vocab(vocab_chars, config.filename_chars)
Procedure to build data

    You MUST RUN this procedure. It iterates over the whole dataset (train,
    dev and test) and extract the vocabularies in terms of words, tags, and
    characters. Having built the vocabularies it writes them in a file. The
    writing of vocabulary in a file assigns an id (the line #) to each word.
    It then extract the relevant GloVe vectors and stores them in a np array
    such that the i-th entry corresponds to the i-th word in the vocabulary.


    Args:
        config: (instance of Config) has attributes like hyper-params...

    
Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Procedure to build data You MUST RUN this procedure . It iterates over the whole dataset ( train dev and test ) and extract the vocabularies in terms of words tags and characters . Having built the vocabularies it writes them in a file . The writing of vocabulary in a file assigns an id ( the line # ) to each word . It then extract the relevant GloVe vectors and stores them in a np array such that the i - th entry corresponds to the i - th word in the vocabulary .
def main():
    config = Config(load=False)
    processing_word = get_processing_word(lowercase=True)
    dev   = CoNLLDataset(config.filename_dev, processing_word)
    test  = CoNLLDataset(config.filename_test, processing_word)
    train = CoNLLDataset(config.filename_train, processing_word)
    vocab_words, vocab_tags = get_vocabs([train, dev, test])
    vocab_word2vec = get_word2vec_vocab(config.filename_word2vec)
    vocab = vocab_words & vocab_word2vec
    vocab.add(str(UNK.encode('utf-8')))
    vocab.add(str(NUM.encode('utf-8')))
    write_vocab(vocab, config.filename_words)
    write_vocab(vocab_tags, config.filename_tags)
    vocab = load_vocab(config.filename_words)
    export_trimmed_word2vec_vectors(vocab, config.filename_word2vec,
                                config.filename__word2vec_trimmed, config.dim_word)
    train = CoNLLDataset(config.filename_train)
    vocab_chars = get_char_vocab(train)
    write_vocab(vocab_chars, config.filename_chars)
def main ( ) : config = Config ( load = False ) processing_word = get_processing_word ( lowercase = True ) dev = CoNLLDataset ( config . filename_dev , processing_word ) test = CoNLLDataset ( config . filename_test , processing_word ) train = CoNLLDataset ( config . filename_train , processing_word ) vocab_words , vocab_tags = get_vocabs ( [ train , dev , test ] ) vocab_word2vec = get_word2vec_vocab ( config . filename_word2vec ) vocab = vocab_words & vocab_word2vec vocab . add ( str ( UNK . encode ( 'utf-8' ) ) ) vocab . add ( str ( NUM . encode ( 'utf-8' ) ) ) write_vocab ( vocab , config . filename_words ) write_vocab ( vocab_tags , config . filename_tags ) vocab = load_vocab ( config . filename_words ) export_trimmed_word2vec_vectors ( vocab , config . filename_word2vec , config . filename__word2vec_trimmed , config . dim_word ) train = CoNLLDataset ( config . filename_train ) vocab_chars = get_char_vocab ( train ) write_vocab ( vocab_chars , config . filename_chars )
Procedure to build data
You MUST RUN this procedure.
Procedure to build data You MUST RUN this procedure .
"""Procedure to build data

    You MUST RUN this procedure. It iterates over the whole dataset (train,
    dev and test) and extract the vocabularies in terms of words, tags, and
    characters. Having built the vocabularies it writes them in a file. The
    writing of vocabulary in a file assigns an id (the line #) to each word.
    It then extract the relevant GloVe vectors and stores them in a np array
    such that the i-th entry corresponds to the i-th word in the vocabulary.


    Args:
        config: (instance of Config) has attributes like hyper-params...

    """ # get config and processing of words # Generators # Build Word and Tag vocab # Save vocab # Trim GloVe Vectors # Build and save char vocab
config
(instance of Config) has attributes like hyper-params
( instance of Config ) has attributes like hyper - params