model = CNN_CNN_LSTM(word_vocab_size, word_embedding_dim, word_out_channels, char_vocab_size,
char_embedding_dim, char_out_channels, decoder_hidden_units,
tag_to_id, pretrained = word_embeds)
elif (model_name == 'CNN_CNN_LSTM_MC'):
print ('CNN_CNN_LSTM_MC')
word_vocab_size = len(word_to_id)
word_embedding_dim = parameters['wrdim']
word_out_channels = parameters['wdchl']
char_vocab_size = len(char_to_id)
char_embedding_dim = parameters['chdim']
char_out_channels = parameters['cnchl']
decoder_hidden_units = parameters['dchid']
model = CNN_CNN_LSTM_MC(word_vocab_size, word_embedding_dim, word_out_channels, char_vocab_size,
char_embedding_dim, char_out_channels, decoder_hidden_units,
tag_to_id, pretrained = word_embeds)
elif (model_name == 'CNN_CNN_LSTM_BB'):
print ('CNN_CNN_LSTM_BB')
word_vocab_size = len(word_to_id)
word_embedding_dim = parameters['wrdim']
word_out_channels = parameters['wdchl']
char_vocab_size = len(char_to_id)
char_embedding_dim = parameters['chdim']
char_out_channels = parameters['cnchl']
decoder_hidden_units = parameters['dchid']
sigma_prior = parameters['sigmp']
model = CNN_CNN_LSTM_BB(word_vocab_size, word_embedding_dim, word_out_channels, char_vocab_size,
char_embedding_dim, char_out_channels, decoder_hidden_units,
def make_model(config, mappings, result_path):
word_to_id = mappings['word_to_id']
tag_to_id = mappings['tag_to_id']
char_to_id = mappings['char_to_id']
word_embeds = mappings['word_embeds']
if config.opt.reload:
log.info(
'Loading Saved Weights....................................................................'
)
model_path = os.path.join(result_path, config.opt.usemodel,
config.opt.checkpoint, 'modelweights')
model = torch.load(model_path)
else:
log.info(
'Building Model............................................................................'
)
log.info(config.opt.usemodel)
word_vocab_size = len(word_to_id)
char_vocab_size = len(char_to_id)
word_embedding_dim = config.parameters.wrdim
char_out_channels = config.parameters.cnchl
char_embedding_dim = config.parameters.chdim
if (config.opt.usemodel == 'CNN_BiLSTM_CRF'):
word_hidden_dim = config.parameters.wldim
model = CNN_BiLSTM_CRF(word_vocab_size,
word_embedding_dim,
word_hidden_dim,
char_vocab_size,
char_embedding_dim,
char_out_channels,
tag_to_id,
pretrained=word_embeds)
elif (config.opt.usemodel == 'CNN_BiLSTM_CRF_MC'):
word_hidden_dim = config.parameters['wldim']
model = CNN_BiLSTM_CRF_MC(word_vocab_size,
word_embedding_dim,
word_hidden_dim,
char_vocab_size,
char_embedding_dim,
char_out_channels,
tag_to_id,
pretrained=word_embeds)
elif (config.opt.usemodel == 'CNN_BiLSTM_CRF_BB'):
word_hidden_dim = config.parameters['wldim']
sigma_prior = config.parameters['sigmp']
model = CNN_BiLSTM_CRF_BB(word_vocab_size,
word_embedding_dim,
word_hidden_dim,
char_vocab_size,
char_embedding_dim,
char_out_channels,
tag_to_id,
sigma_prior=sigma_prior,
pretrained=word_embeds)
elif (config.opt.usemodel == 'CNN_CNN_LSTM'):
word_out_channels = config.parameters['wdchl']
decoder_hidden_units = config.parameters['dchid']
model = CNN_CNN_LSTM(word_vocab_size,
word_embedding_dim,
word_out_channels,
char_vocab_size,
char_embedding_dim,
char_out_channels,
decoder_hidden_units,
tag_to_id,
pretrained=word_embeds)
elif (config.opt.usemodel == 'CNN_CNN_LSTM_MC'):
word_out_channels = config.parameters['wdchl']
decoder_hidden_units = config.parameters['dchid']
model = CNN_CNN_LSTM_MC(word_vocab_size,
word_embedding_dim,
word_out_channels,
char_vocab_size,
char_embedding_dim,
char_out_channels,
decoder_hidden_units,
tag_to_id,
pretrained=word_embeds)
elif (config.opt.usemodel == 'CNN_CNN_LSTM_BB'):
word_out_channels = config.parameters['wdchl']
decoder_hidden_units = config.parameters['dchid']
sigma_prior = config.parameters['sigmp']
model = CNN_CNN_LSTM_BB(word_vocab_size,
word_embedding_dim,
word_out_channels,
char_vocab_size,
char_embedding_dim,
char_out_channels,
decoder_hidden_units,
tag_to_id,
sigma_prior=sigma_prior,
pretrained=word_embeds)
else:
raise KeyError
return model