(self, article, abstract_sentences, vocab, hps)
Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.

    Args:
      article: source text; a string. each token is separated by a single space.
      abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
      vocab: Vocabulary object
      hps: hyperparameters
Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Initializes the Example performing tokenization and truncation to produce the encoder decoder and target sequences which are stored in self .
def __init__(self, article, abstract_sentences, vocab, hps):
    """Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.

    Args:
      article: source text; a string. each token is separated by a single space.
      abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
      vocab: Vocabulary object
      hps: hyperparameters
    """
    self.hps = hps

    # Get ids of special tokens
    start_decoding = vocab.word2id(data.START_DECODING)
    stop_decoding = vocab.word2id(data.STOP_DECODING)

    # Process the article
    article_words = article.split()
    if len(article_words) > hps.max_enc_steps:
      article_words = article_words[:hps.max_enc_steps]
    self.enc_len = len(article_words) # store the length after truncation but before padding
    self.enc_input = [vocab.word2id(w) for w in article_words] # list of word ids; OOVs are represented by the id for UNK token

    # Process the abstract
    abstract = ' '.join(abstract_sentences) # string
    abstract_words = abstract.split() # list of strings
    abs_ids = [vocab.word2id(w) for w in abstract_words] # list of word ids; OOVs are represented by the id for UNK token

    # Get the decoder input sequence and target sequence
    self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
    self.dec_len = len(self.dec_input)

    # If using pointer-generator mode, we need to store some extra info
    if hps.pointer_gen:
      # Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
      self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab)

      # Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
      abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab, self.article_oovs)

      # Overwrite decoder target sequence so it uses the temp article OOV ids
      _, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, hps.max_dec_steps, start_decoding, stop_decoding)

    # Store the original strings
    self.original_article = article
    self.original_abstract = abstract
    self.original_abstract_sents = abstract_sentences
def __init__ ( self , article , abstract_sentences , vocab , hps ) : self . hps = hps # Get ids of special tokens start_decoding = vocab . word2id ( data . START_DECODING ) stop_decoding = vocab . word2id ( data . STOP_DECODING ) # Process the article article_words = article . split ( ) if len ( article_words ) > hps . max_enc_steps : article_words = article_words [ : hps . max_enc_steps ] self . enc_len = len ( article_words ) # store the length after truncation but before padding self . enc_input = [ vocab . word2id ( w ) for w in article_words ] # list of word ids; OOVs are represented by the id for UNK token # Process the abstract abstract = ' ' . join ( abstract_sentences ) # string abstract_words = abstract . split ( ) # list of strings abs_ids = [ vocab . word2id ( w ) for w in abstract_words ] # list of word ids; OOVs are represented by the id for UNK token # Get the decoder input sequence and target sequence self . dec_input , self . target = self . get_dec_inp_targ_seqs ( abs_ids , hps . max_dec_steps , start_decoding , stop_decoding ) self . dec_len = len ( self . dec_input ) # If using pointer-generator mode, we need to store some extra info if hps . pointer_gen : # Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves self . enc_input_extend_vocab , self . article_oovs = data . article2ids ( article_words , vocab ) # Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id abs_ids_extend_vocab = data . abstract2ids ( abstract_words , vocab , self . article_oovs ) # Overwrite decoder target sequence so it uses the temp article OOV ids _ , self . target = self . get_dec_inp_targ_seqs ( abs_ids_extend_vocab , hps . max_dec_steps , start_decoding , stop_decoding ) # Store the original strings self . original_article = article self . original_abstract = abstract self . original_abstract_sents = abstract_sentences