SYMBOL INDEX (331 symbols across 31 files)

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/attention_decoder.py
  function attention_decoder (line 27) | def attention_decoder(decoder_inputs, initial_state, encoder_states, enc...
  function linear (line 184) | def linear(args, output_size, bias, bias_start=0.0, scope=None):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/batcher.py
  class Example (line 28) | class Example(object):
    method __init__ (line 31) | def __init__(self, article, abstract_sentences, vocab, hps):
    method get_dec_inp_targ_seqs (line 79) | def get_dec_inp_targ_seqs(self, sequence, max_len, start_id, stop_id):
    method pad_decoder_inp_targ (line 103) | def pad_decoder_inp_targ(self, max_len, pad_id):
    method pad_encoder_input (line 111) | def pad_encoder_input(self, max_len, pad_id):
  class Batch (line 120) | class Batch(object):
    method __init__ (line 123) | def __init__(self, example_list, hps, vocab):
    method init_encoder_seq (line 136) | def init_encoder_seq(self, example_list, hps):
    method init_decoder_seq (line 184) | def init_decoder_seq(self, example_list, hps):
    method store_orig_strings (line 210) | def store_orig_strings(self, example_list):
  class Batcher (line 217) | class Batcher(object):
    method __init__ (line 222) | def __init__(self, data_path, vocab, hps, single_pass):
    method next_batch (line 271) | def next_batch(self):
    method fill_example_queue (line 289) | def fill_example_queue(self):
    method fill_batch_queue (line 311) | def fill_batch_queue(self):
    method watch_threads (line 339) | def watch_threads(self):
    method text_generator (line 359) | def text_generator(self, example_generator):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/beam_search.py
  class Hypothesis (line 25) | class Hypothesis(object):
    method __init__ (line 28) | def __init__(self, tokens, log_probs, state, attn_dists, p_gens, cover...
    method extend (line 46) | def extend(self, token, log_prob, state, attn_dist, p_gen, coverage):
    method latest_token (line 67) | def latest_token(self):
    method log_prob (line 71) | def log_prob(self):
    method avg_log_prob (line 76) | def avg_log_prob(self):
  function run_beam_search (line 81) | def run_beam_search(sess, model, vocab, batch):
  function sort_hyps (line 164) | def sort_hyps(hyps):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/data.py
  class Vocab (line 37) | class Vocab(object):
    method __init__ (line 40) | def __init__(self, vocab_file, max_size):
    method word2id (line 77) | def word2id(self, word):
    method id2word (line 83) | def id2word(self, word_id):
    method size (line 89) | def size(self):
    method write_metadata (line 93) | def write_metadata(self, fpath):
  function example_generator (line 108) | def example_generator(data_path, single_pass):
  function article2ids (line 144) | def article2ids(article_words, vocab):
  function abstract2ids (line 171) | def abstract2ids(abstract_words, vocab, article_oovs):
  function outputids2words (line 196) | def outputids2words(id_list, vocab, article_oovs):
  function abstract2sents (line 222) | def abstract2sents(abstract):
  function show_art_oovs (line 242) | def show_art_oovs(article, vocab):
  function show_abs_oovs (line 251) | def show_abs_oovs(abstract, vocab, article_oovs):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/decode.py
  class BeamSearchDecoder (line 35) | class BeamSearchDecoder(object):
    method __init__ (line 38) | def __init__(self, model, batcher, vocab):
    method decode (line 77) | def decode(self):
    method write_for_rouge (line 127) | def write_for_rouge(self, reference_sents, decoded_words, ex_index):
    method write_for_attnvis (line 165) | def write_for_attnvis(self, article, abstract, decoded_words, attn_dis...
  function print_results (line 192) | def print_results(article, abstract, decoded_output):
  function make_html_safe (line 201) | def make_html_safe(s):
  function rouge_eval (line 208) | def rouge_eval(ref_dir, dec_dir):
  function rouge_log (line 220) | def rouge_log(results_dict, dir_to_write):
  function get_decode_dir_name (line 243) | def get_decode_dir_name(ckpt_name):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/model.py
  class SummarizationModel (line 28) | class SummarizationModel(object):
    method __init__ (line 31) | def __init__(self, hps, vocab):
    method _add_placeholders (line 35) | def _add_placeholders(self):
    method _make_feed_dict (line 56) | def _make_feed_dict(self, batch, just_enc=False):
    method _add_encoder (line 76) | def _add_encoder(self, encoder_inputs, seq_len):
    method _reduce_states (line 97) | def _reduce_states(self, fw_st, bw_st):
    method _add_decoder (line 124) | def _add_decoder(self, inputs):
    method _calc_final_dist (line 146) | def _calc_final_dist(self, vocab_dists, attn_dists):
    method _add_emb_vis (line 185) | def _add_emb_vis(self, embedding_var):
    method _add_seq2seq (line 199) | def _add_seq2seq(self):
    method _add_train_op (line 288) | def _add_train_op(self):
    method build_graph (line 309) | def build_graph(self):
    method run_train_step (line 323) | def run_train_step(self, sess, batch):
    method run_eval_step (line 336) | def run_eval_step(self, sess, batch):
    method run_encoder (line 348) | def run_encoder(self, sess, batch):
    method decode_onestep (line 368) | def decode_onestep(self, sess, batch, latest_tokens, enc_states, dec_i...
  function _mask_and_avg (line 447) | def _mask_and_avg(values, padding_mask):
  function _coverage_loss (line 464) | def _coverage_loss(attn_dists, padding_mask):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/run_summarization.py
  function calc_running_avg_loss (line 83) | def calc_running_avg_loss(loss, running_avg_loss, summary_writer, step, ...
  function restore_best_model (line 110) | def restore_best_model():
  function convert_to_coverage_model (line 135) | def convert_to_coverage_model():
  function setup_training (line 159) | def setup_training(model, batcher):
  function run_training (line 190) | def run_training(model, batcher, sess_context_manager, sv, summary_writer):
  function run_eval (line 228) | def run_eval(model, batcher, vocab):
  function main (line 275) | def main(unused_argv):

FILE: P007PytorchPointerGeneratorNetwork/pointer-generator/util.py
  function get_config (line 24) | def get_config():
  function load_ckpt (line 30) | def load_ckpt(saver, sess, ckpt_dir="train"):

FILE: P010Model/model_transformer.py
  class EncoderDecoder (line 22) | class EncoderDecoder(nn.Module):
    method __init__ (line 26) | def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
    method forward (line 34) | def forward(self, src, tgt, src_mask, tgt_mask):
    method encode (line 42) | def encode(self, src, src_mask):
    method decode (line 45) | def decode(self, memory, src_mask, tgt, tgt_mask):
  class Generator (line 49) | class Generator(nn.Module):
    method __init__ (line 54) | def __init__(self, d_model, vocab):
    method forward (line 58) | def forward(self, x):
  function clones (line 63) | def clones(module, N):
  class Encoder (line 71) | class Encoder(nn.Module):
    method __init__ (line 73) | def __init__(self, layer, N):
    method forward (line 78) | def forward(self, x, mask):
  class LayerNorm (line 84) | class LayerNorm(nn.Module):
    method __init__ (line 86) | def __init__(self, features, eps=1e-6):
    method forward (line 92) | def forward(self, x):
  class SublayerConnection (line 98) | class SublayerConnection(nn.Module):
    method __init__ (line 103) | def __init__(self, size, dropout):
    method forward (line 108) | def forward(self, x, sublayer):
  class EncoderLayer (line 112) | class EncoderLayer(nn.Module):
    method __init__ (line 114) | def __init__(self, size, self_attn, feed_forward, dropout):
    method forward (line 121) | def forward(self, x, mask):
  class Decoder (line 129) | class Decoder(nn.Module):
    method __init__ (line 131) | def __init__(self, layer, N):
    method forward (line 136) | def forward(self, x, memory, src_mask, tgt_mask):
  class DecoderLayer (line 142) | class DecoderLayer(nn.Module):
    method __init__ (line 144) | def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
    method forward (line 152) | def forward(self, x, memory, src_mask, tgt_mask):
  function subsequent_mask (line 159) | def subsequent_mask(size):
  function attention (line 167) | def attention(query, key, value, mask=None, dropout=None):
  class MultiHeadedAttention (line 180) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 181) | def __init__(self, h, d_model, dropout=0.1):
    method forward (line 192) | def forward(self, query, key, value, mask=None):
    method __init__ (line 289) | def __init__(self, h, d_model, dropout=0.1):
    method forward (line 300) | def forward(self, query, key, value, mask=None):
  class PositionwiseFeedForward (line 213) | class PositionwiseFeedForward(nn.Module):
    method __init__ (line 215) | def __init__(self, d_model, d_ff, dropout=0.1):
    method forward (line 221) | def forward(self, x):
  class Embeddings (line 225) | class Embeddings(nn.Module):
    method __init__ (line 226) | def __init__(self, d_model, vocab):
    method forward (line 231) | def forward(self, x):
  class PositionalEncoding (line 236) | class PositionalEncoding(nn.Module):
    method __init__ (line 238) | def __init__(self, d_model, dropout, max_len=5000):
    method forward (line 253) | def forward(self, x):
  function make_model (line 261) | def make_model(src_vocab, tgt_vocab, N=6,
  class MultiHeadedAttention (line 288) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 181) | def __init__(self, h, d_model, dropout=0.1):
    method forward (line 192) | def forward(self, query, key, value, mask=None):
    method __init__ (line 289) | def __init__(self, h, d_model, dropout=0.1):
    method forward (line 300) | def forward(self, query, key, value, mask=None):
  class Batch (line 398) | class Batch(object):
    method __init__ (line 400) | def __init__(self, src, trg=None, pad=0):
    method make_std_mask (line 410) | def make_std_mask(tgt, pad):
  function run_epoch (line 427) | def run_epoch(data_iter, model, loss_compute, device):
  function batch_size_fn (line 462) | def batch_size_fn(new, count, sofar):
  class NoamOpt (line 490) | class NoamOpt(object):
    method __init__ (line 492) | def __init__(self, model_size, factor, warmup, optimizer):
    method step (line 500) | def step(self):
    method rate (line 509) | def rate(self, step = None):
  function get_std_opt (line 516) | def get_std_opt(model):
  class LabelSmoothing (line 543) | class LabelSmoothing(nn.Module):
    method __init__ (line 545) | def __init__(self, size, padding_idx, smoothing=0.0):
    method forward (line 554) | def forward(self, x, target):
  function data_gen (line 605) | def data_gen(V, batch, nbatches):
  class SimpleLossCompute (line 619) | class SimpleLossCompute(object):
    method __init__ (line 621) | def __init__(self, generator, criterion, opt=None):
    method __call__ (line 626) | def __call__(self, x, y, norm):

FILE: named_entity_recognition/data.py
  function build_corpus (line 5) | def build_corpus(split, make_vocab=True, data_dir="./ResumeNER"):
  function build_map (line 34) | def build_map(lists):

FILE: named_entity_recognition/evaluate.py
  function hmm_train_eval (line 11) | def hmm_train_eval(train_data, test_data, word2id, tag2id, remove_O=False):
  function crf_train_eval (line 36) | def crf_train_eval(train_data, test_data, remove_O=False):
  function bilstm_train_and_eval (line 55) | def bilstm_train_and_eval(train_data, dev_data, test_data,
  function ensemble_evaluate (line 83) | def ensemble_evaluate(results, targets, remove_O=False):

FILE: named_entity_recognition/evaluating.py
  class Metrics (line 6) | class Metrics(object):
    method __init__ (line 9) | def __init__(self, golden_tags, predict_tags, remove_O=False):
    method cal_precision (line 33) | def cal_precision(self):
    method cal_recall (line 42) | def cal_recall(self):
    method cal_f1 (line 50) | def cal_f1(self):
    method report_scores (line 57) | def report_scores(self):
    method count_correct_tags (line 98) | def count_correct_tags(self):
    method _cal_weighted_average (line 110) | def _cal_weighted_average(self):
    method _remove_Otags (line 130) | def _remove_Otags(self):
    method report_confusion_matrix (line 147) | def report_confusion_matrix(self):

FILE: named_entity_recognition/main.py
  function main (line 8) | def main():

FILE: named_entity_recognition/models/bilstm.py
  class BiLSTM (line 6) | class BiLSTM(nn.Module):
    method __init__ (line 7) | def __init__(self, vocab_size, emb_size, hidden_size, out_size):
    method forward (line 22) | def forward(self, sents_tensor, lengths):
    method test (line 34) | def test(self, sents_tensor, lengths, _):

FILE: named_entity_recognition/models/bilstm_crf.py
  class BILSTM_Model (line 13) | class BILSTM_Model(object):
    method __init__ (line 14) | def __init__(self, vocab_size, out_size, crf=True):
    method train (line 52) | def train(self, word_lists, tag_lists,
    method train_step (line 85) | def train_step(self, batch_sents, batch_tags, word2id, tag2id):
    method validate (line 105) | def validate(self, dev_word_lists, dev_tag_lists, word2id, tag2id):
    method test (line 137) | def test(self, word_lists, tag_lists, word2id, tag2id):
  class BiLSTM_CRF (line 174) | class BiLSTM_CRF(nn.Module):
    method __init__ (line 175) | def __init__(self, vocab_size, emb_size, hidden_size, out_size):
    method forward (line 190) | def forward(self, sents_tensor, lengths):
    method test (line 203) | def test(self, test_sents_tensor, lengths, tag2id):

FILE: named_entity_recognition/models/config.py
  class TrainingConfig (line 2) | class TrainingConfig(object):
  class LSTMConfig (line 10) | class LSTMConfig(object):

FILE: named_entity_recognition/models/crf.py
  class CRFModel (line 6) | class CRFModel(object):
    method __init__ (line 7) | def __init__(self,
    method train (line 21) | def train(self, sentences, tag_lists):
    method test (line 25) | def test(self, sentences):

FILE: named_entity_recognition/models/hmm.py
  class HMM (line 4) | class HMM(object):
    method __init__ (line 5) | def __init__(self, N, M):
    method train (line 20) | def train(self, word_lists, tag_lists, word2id, tag2id):
    method test (line 62) | def test(self, word_lists, word2id, tag2id):
    method decoding (line 69) | def decoding(self, word_list, word2id, tag2id):

FILE: named_entity_recognition/models/util.py
  function word2features (line 7) | def word2features(sent, i):
  function sent2features (line 26) | def sent2features(sent):
  function tensorized (line 33) | def tensorized(batch, maps):
  function sort_by_lengths (line 50) | def sort_by_lengths(word_lists, tag_lists):
  function cal_loss (line 63) | def cal_loss(logits, targets, tag2id):
  function cal_lstm_crf_loss (line 88) | def cal_lstm_crf_loss(crf_scores, targets, tag2id):
  function indexed (line 155) | def indexed(targets, tagset_size, start_id):

FILE: named_entity_recognition/test.py
  function main (line 14) | def main():

FILE: named_entity_recognition/utils.py
  function merge_maps (line 4) | def merge_maps(dict1, dict2):
  function save_model (line 12) | def save_model(model, file_name):
  function load_model (line 18) | def load_model(file_name):
  function extend_maps (line 27) | def extend_maps(word2id, tag2id, for_crf=True):
  function prepocess_data_for_lstmcrf (line 42) | def prepocess_data_for_lstmcrf(word_lists, tag_lists, test=False):
  function flatten_lists (line 52) | def flatten_lists(lists):

FILE: word2vector/fasttext/fasttext.py
  function train_sentence_sg (line 43) | def train_sentence_sg(model, sentence, alpha, is_ft=False, work=None):
  function train_sentence_cbow (line 163) | def train_sentence_cbow(model, sentence, alpha, is_ft=False, work=None, ...
  class Vocab (line 284) | class Vocab(object):
    method __init__ (line 288) | def __init__(self, **kwargs):
    method __lt__ (line 305) | def __lt__(self, vocab):
    method __str__ (line 308) | def __str__(self):
  class FastText (line 315) | class FastText(SaveAndLoad):
    method __init__ (line 316) | def __init__(self, sentences=None, size=100, alpha=0.025,
    method get_latest_training_loss (line 384) | def get_latest_training_loss(self):
    method __reset_vocab (line 387) | def __reset_vocab(self):
    method build_vocab (line 395) | def build_vocab(self, sentences):
    method build_ngrams (line 426) | def build_ngrams(self):
    method reset_weights (line 452) | def reset_weights(self):
    method make_cum_table (line 475) | def make_cum_table(self, power=0.75, domain=2**31 - 1):
    method create_binary_tree (line 496) | def create_binary_tree(self):
    method train (line 536) | def train(self, sentences, total_words=None, word_count=0, chunksize=1...
    method __getitem__ (line 623) | def __getitem__(self, word):
    method __contains__ (line 632) | def __contains__(self, word):
    method similarity (line 636) | def similarity(self, w1, w2):
    method init_sims (line 648) | def init_sims(self):
    method most_similar (line 657) | def most_similar(self, positive=[], negative=[], topn=10):
    method word_vec (line 695) | def word_vec(self, word, use_norm=False):
    method compute_ngrams (line 723) | def compute_ngrams(self, word, min_n, max_n):
    method ft_hash (line 747) | def ft_hash(self, string):

FILE: word2vector/fasttext/utils.py
  function zeros_aligned (line 22) | def zeros_aligned(shape, dtype, order='C', align=128):
  function chunkize_serial (line 30) | def chunkize_serial(iterable, chunksize, as_numpy=False):
  function expit (line 53) | def expit(x):
  function unitvec (line 57) | def unitvec(vec):
  function cossim (line 81) | def cossim(vec1, vec2):
  class RepeatCorpusNTimes (line 95) | class RepeatCorpusNTimes(object):
    method __init__ (line 104) | def __init__(self, corpus, n):
    method __iter__ (line 116) | def __iter__(self):
  class SaveAndLoad (line 122) | class SaveAndLoad(object):
    method __init__ (line 126) | def __init__(self):
    method save (line 129) | def save(self, fname):
    method load (line 135) | def load(cls, fname):
  class Text8Corpus (line 142) | class Text8Corpus(object):
    method __init__ (line 148) | def __init__(self, fname, sent_num=None, sent_len=1000):
    method __iter__ (line 156) | def __iter__(self):

FILE: word2vector/fasttext/wget.py
  function filename_from_url (line 36) | def filename_from_url(url):
  function filename_from_headers (line 43) | def filename_from_headers(headers):
  function filename_fix_existing (line 72) | def filename_fix_existing(filename):
  function get_console_width (line 94) | def get_console_width():
  function bar_thermometer (line 152) | def bar_thermometer(current, total, width=80):
  function bar_adaptive (line 166) | def bar_adaptive(current, total, width=80):
  function callback_progress (line 252) | def callback_progress(blocks, block_size, total_size, bar_function):
  class ThrowOnErrorOpener (line 282) | class ThrowOnErrorOpener(urllib.FancyURLopener):
    method http_error_default (line 283) | def http_error_default(self, url, fp, errcode, errmsg, headers):
  function download (line 286) | def download(url, out=None, bar=bar_adaptive):

FILE: word2vector/glove/glove.py
  class Glove (line 42) | class Glove(SaveAndLoad):
    method __init__ (line 43) | def __init__(self, corpus=None, size=100, alpha=0.05,
    method build_vocab (line 99) | def build_vocab(self, corpus):
    method build_cooccurance_matrix (line 114) | def build_cooccurance_matrix(self, corpus):
    method iter_cooccurance_matrix (line 135) | def iter_cooccurance_matrix(self):
    method train (line 149) | def train(self, x_max=100, power=0.75):
    method reset_params (line 234) | def reset_params(self):
    method most_similar (line 246) | def most_similar(self, positive=[], negative=[], topn=10):
    method get_vocab (line 286) | def get_vocab(self):
    method get_embeddings (line 293) | def get_embeddings(self):

FILE: word2vector/glove/utils.py
  function zeros_aligned (line 22) | def zeros_aligned(shape, dtype, order='C', align=128):
  function chunkize_serial (line 30) | def chunkize_serial(iterable, chunksize, as_numpy=False):
  function expit (line 53) | def expit(x):
  function unitvec (line 57) | def unitvec(vec):
  function cossim (line 81) | def cossim(vec1, vec2):
  class RepeatCorpusNTimes (line 95) | class RepeatCorpusNTimes(object):
    method __init__ (line 104) | def __init__(self, corpus, n):
    method __iter__ (line 116) | def __iter__(self):
  class SaveAndLoad (line 122) | class SaveAndLoad(object):
    method __init__ (line 126) | def __init__(self):
    method save (line 129) | def save(self, fname):
    method load (line 135) | def load(cls, fname):
  class Text8Corpus (line 142) | class Text8Corpus(object):
    method __init__ (line 148) | def __init__(self, fname, sent_num=None, sent_len=1000):
    method __iter__ (line 156) | def __iter__(self):

FILE: word2vector/glove/wget.py
  function filename_from_url (line 36) | def filename_from_url(url):
  function filename_from_headers (line 43) | def filename_from_headers(headers):
  function filename_fix_existing (line 72) | def filename_fix_existing(filename):
  function get_console_width (line 94) | def get_console_width():
  function bar_thermometer (line 152) | def bar_thermometer(current, total, width=80):
  function bar_adaptive (line 166) | def bar_adaptive(current, total, width=80):
  function callback_progress (line 252) | def callback_progress(blocks, block_size, total_size, bar_function):
  class ThrowOnErrorOpener (line 282) | class ThrowOnErrorOpener(urllib.FancyURLopener):
    method http_error_default (line 283) | def http_error_default(self, url, fp, errcode, errmsg, headers):
  function download (line 286) | def download(url, out=None, bar=bar_adaptive):

FILE: word2vector/ngrams/lm.py
  class Test (line 45) | class Test(unittest.TestCase):
    method setUp (line 49) | def setUp(self):
    method tearDown (line 55) | def tearDown(self):
    method test_prob (line 58) | def test_prob(self):
    method test_perplexity (line 62) | def test_perplexity(self):
  function test (line 66) | def test():

FILE: word2vector/word2vec/utils.py
  function zeros_aligned (line 22) | def zeros_aligned(shape, dtype, order='C', align=128):
  function chunkize_serial (line 30) | def chunkize_serial(iterable, chunksize, as_numpy=False):
  function expit (line 53) | def expit(x):
  function unitvec (line 57) | def unitvec(vec):
  function cossim (line 81) | def cossim(vec1, vec2):
  class RepeatCorpusNTimes (line 95) | class RepeatCorpusNTimes(object):
    method __init__ (line 104) | def __init__(self, corpus, n):
    method __iter__ (line 116) | def __iter__(self):
  class SaveAndLoad (line 122) | class SaveAndLoad(object):
    method __init__ (line 126) | def __init__(self):
    method save (line 129) | def save(self, fname):
    method load (line 135) | def load(cls, fname):
  class Text8Corpus (line 142) | class Text8Corpus(object):
    method __init__ (line 148) | def __init__(self, fname, sent_num=None, sent_len=1000):
    method __iter__ (line 156) | def __iter__(self):

FILE: word2vector/word2vec/wget.py
  function filename_from_url (line 36) | def filename_from_url(url):
  function filename_from_headers (line 43) | def filename_from_headers(headers):
  function filename_fix_existing (line 72) | def filename_fix_existing(filename):
  function get_console_width (line 94) | def get_console_width():
  function bar_thermometer (line 152) | def bar_thermometer(current, total, width=80):
  function bar_adaptive (line 166) | def bar_adaptive(current, total, width=80):
  function callback_progress (line 252) | def callback_progress(blocks, block_size, total_size, bar_function):
  class ThrowOnErrorOpener (line 282) | class ThrowOnErrorOpener(urllib.FancyURLopener):
    method http_error_default (line 283) | def http_error_default(self, url, fp, errcode, errmsg, headers):
  function download (line 286) | def download(url, out=None, bar=bar_adaptive):

FILE: word2vector/word2vec/word2vec.py
  function train_sentence_sg (line 44) | def train_sentence_sg(model, sentence, alpha, work=None):
  function train_sentence_cbow (line 134) | def train_sentence_cbow(model, sentence, alpha, work=None, cbow_mean=True):
  class Vocab (line 224) | class Vocab(object):
    method __init__ (line 228) | def __init__(self, **kwargs):
    method __lt__ (line 245) | def __lt__(self, vocab):
    method __str__ (line 248) | def __str__(self):
  class Word2Vec (line 254) | class Word2Vec(SaveAndLoad):
    method __init__ (line 255) | def __init__(self, sentences=None, size=100, alpha=0.025,
    method get_latest_training_loss (line 307) | def get_latest_training_loss(self):
    method __reset_vocab (line 311) | def __reset_vocab(self):
    method build_vocab (line 318) | def build_vocab(self, sentences):
    method reset_weights (line 347) | def reset_weights(self):
    method make_cum_table (line 367) | def make_cum_table(self, power=0.75, domain=2**31 - 1):
    method create_binary_tree (line 388) | def create_binary_tree(self):
    method train (line 428) | def train(self, sentences, total_words=None, word_count=0, chunksize=1...
    method __getitem__ (line 515) | def __getitem__(self, word):
    method __contains__ (line 524) | def __contains__(self, word):
    method similarity (line 528) | def similarity(self, w1, w2):
    method init_sims (line 540) | def init_sims(self):
    method most_similar (line 546) | def most_similar(self, positive=[], negative=[], topn=10):
    method accuracy (line 587) | def accuracy(self, questions, restrict_vocab=30000):