jblitzar/github-python · Datasets at Hugging Face

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print( "\tpre-", o.precondition_neg )
print( "\teff+", o.effect_pos )
print( "\teff-", o.effect_neg )
print()


if __name__ == '__main__':
args = parser.parse_args()
for n in args.demonumber:
run_demo(n)

# <FILESEP>
__author__ = 'max'

import time
import sys
import argparse
from lasagne_nlp.utils import utils
import lasagne_nlp.utils.data_processor as data_processor
from lasagne_nlp.utils.objectives import crf_loss, crf_accuracy
import lasagne
import theano
import theano.tensor as T
from lasagne_nlp.networks.networks import build_BiLSTM_CNN_CRF

import numpy as np


def main():
parser = argparse.ArgumentParser(description='Tuning with bi-directional LSTM-CNN-CRF')
parser.add_argument('--fine_tune', action='store_true', help='Fine tune the word embeddings')
parser.add_argument('--embedding', choices=['word2vec', 'glove', 'senna', 'random'], help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', default=None, help='path for embedding dict')
parser.add_argument('--batch_size', type=int, default=10, help='Number of sentences in each batch')
parser.add_argument('--num_units', type=int, default=100, help='Number of hidden units in LSTM')
parser.add_argument('--num_filters', type=int, default=20, help='Number of filters in CNN')
parser.add_argument('--learning_rate', type=float, default=0.1, help='Learning rate')
parser.add_argument('--decay_rate', type=float, default=0.1, help='Decay rate of learning rate')
parser.add_argument('--grad_clipping', type=float, default=0, help='Gradient clipping')
parser.add_argument('--gamma', type=float, default=1e-6, help='weight for regularization')
parser.add_argument('--peepholes', action='store_true', help='Peepholes for LSTM')
parser.add_argument('--oov', choices=['random', 'embedding'], help='Embedding for oov word', required=True)
parser.add_argument('--update', choices=['sgd', 'momentum', 'nesterov', 'adadelta'], help='update algorithm',
default='sgd')
parser.add_argument('--regular', choices=['none', 'l2'], help='regularization for training', required=True)
parser.add_argument('--dropout', action='store_true', help='Apply dropout layers')
parser.add_argument('--patience', type=int, default=5, help='Patience for early stopping')
parser.add_argument('--output_prediction', action='store_true', help='Output predictions to temp files')
parser.add_argument('--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument('--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument('--test') # "data/POS-penn/wsj/split1/wsj1.test.original"

args = parser.parse_args()

def construct_input_layer():
if fine_tune:
layer_input = lasagne.layers.InputLayer(shape=(None, max_length), input_var=input_var, name='input')
layer_embedding = lasagne.layers.EmbeddingLayer(layer_input, input_size=alphabet_size,
output_size=embedd_dim,
W=embedd_table, name='embedding')
return layer_embedding
else:
layer_input = lasagne.layers.InputLayer(shape=(None, max_length, embedd_dim), input_var=input_var,
name='input')
return layer_input

def construct_char_input_layer():
layer_char_input = lasagne.layers.InputLayer(shape=(None, max_sent_length, max_char_length),
input_var=char_input_var, name='char-input')
layer_char_input = lasagne.layers.reshape(layer_char_input, (-1, [2]))
layer_char_embedding = lasagne.layers.EmbeddingLayer(layer_char_input, input_size=char_alphabet_size,
output_size=char_embedd_dim, W=char_embedd_table,
name='char_embedding')
layer_char_input = lasagne.layers.DimshuffleLayer(layer_char_embedding, pattern=(0, 2, 1))
return layer_char_input

logger = utils.get_logger("BiLSTM-CNN-CRF")
fine_tune = args.fine_tune
oov = args.oov
regular = args.regular
embedding = args.embedding
embedding_path = args.embedding_dict
train_path = args.train
dev_path = args.dev
test_path = args.test
update_algo = args.update
grad_clipping = args.grad_clipping
peepholes = args.peepholes
num_filters = args.num_filters
gamma = args.gamma
output_predict = args.output_prediction
dropout = args.dropout

X_train, Y_train, mask_train, X_dev, Y_dev, mask_dev, X_test, Y_test, mask_test, \
embedd_table, label_alphabet, \
C_train, C_dev, C_test, char_embedd_table = data_processor.load_dataset_sequence_labeling(train_path, dev_path,
test_path, oov=oov,
fine_tune=fine_tune,
embedding=embedding,

print( "\tpre-", o.precondition_neg )

print( "\teff+", o.effect_pos )

print( "\teff-", o.effect_neg )

print()

if __name__ == '__main__':

args = parser.parse_args()

for n in args.demonumber:

run_demo(n)

# <FILESEP>

__author__ = 'max'

import time

import sys

import argparse

from lasagne_nlp.utils import utils

import lasagne_nlp.utils.data_processor as data_processor

from lasagne_nlp.utils.objectives import crf_loss, crf_accuracy

import lasagne

import theano

import theano.tensor as T

from lasagne_nlp.networks.networks import build_BiLSTM_CNN_CRF

import numpy as np

def main():

parser = argparse.ArgumentParser(description='Tuning with bi-directional LSTM-CNN-CRF')

parser.add_argument('--fine_tune', action='store_true', help='Fine tune the word embeddings')

parser.add_argument('--embedding', choices=['word2vec', 'glove', 'senna', 'random'], help='Embedding for words',

required=True)

parser.add_argument('--embedding_dict', default=None, help='path for embedding dict')

parser.add_argument('--batch_size', type=int, default=10, help='Number of sentences in each batch')

parser.add_argument('--num_units', type=int, default=100, help='Number of hidden units in LSTM')

parser.add_argument('--num_filters', type=int, default=20, help='Number of filters in CNN')

parser.add_argument('--learning_rate', type=float, default=0.1, help='Learning rate')

parser.add_argument('--decay_rate', type=float, default=0.1, help='Decay rate of learning rate')

parser.add_argument('--grad_clipping', type=float, default=0, help='Gradient clipping')

parser.add_argument('--gamma', type=float, default=1e-6, help='weight for regularization')

parser.add_argument('--peepholes', action='store_true', help='Peepholes for LSTM')

parser.add_argument('--oov', choices=['random', 'embedding'], help='Embedding for oov word', required=True)

parser.add_argument('--update', choices=['sgd', 'momentum', 'nesterov', 'adadelta'], help='update algorithm',

default='sgd')

parser.add_argument('--regular', choices=['none', 'l2'], help='regularization for training', required=True)

parser.add_argument('--dropout', action='store_true', help='Apply dropout layers')

parser.add_argument('--patience', type=int, default=5, help='Patience for early stopping')

parser.add_argument('--output_prediction', action='store_true', help='Output predictions to temp files')

parser.add_argument('--train') # "data/POS-penn/wsj/split1/wsj1.train.original"

parser.add_argument('--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"

parser.add_argument('--test') # "data/POS-penn/wsj/split1/wsj1.test.original"

args = parser.parse_args()

def construct_input_layer():

if fine_tune:

layer_input = lasagne.layers.InputLayer(shape=(None, max_length), input_var=input_var, name='input')

layer_embedding = lasagne.layers.EmbeddingLayer(layer_input, input_size=alphabet_size,

output_size=embedd_dim,

W=embedd_table, name='embedding')

return layer_embedding

else:

layer_input = lasagne.layers.InputLayer(shape=(None, max_length, embedd_dim), input_var=input_var,

name='input')

return layer_input

def construct_char_input_layer():

layer_char_input = lasagne.layers.InputLayer(shape=(None, max_sent_length, max_char_length),

input_var=char_input_var, name='char-input')

layer_char_input = lasagne.layers.reshape(layer_char_input, (-1, [2]))

layer_char_embedding = lasagne.layers.EmbeddingLayer(layer_char_input, input_size=char_alphabet_size,

output_size=char_embedd_dim, W=char_embedd_table,

name='char_embedding')

layer_char_input = lasagne.layers.DimshuffleLayer(layer_char_embedding, pattern=(0, 2, 1))

return layer_char_input

logger = utils.get_logger("BiLSTM-CNN-CRF")

fine_tune = args.fine_tune

oov = args.oov

regular = args.regular

embedding = args.embedding

embedding_path = args.embedding_dict

train_path = args.train

dev_path = args.dev

test_path = args.test

update_algo = args.update

grad_clipping = args.grad_clipping

peepholes = args.peepholes

num_filters = args.num_filters

gamma = args.gamma

output_predict = args.output_prediction

dropout = args.dropout

X_train, Y_train, mask_train, X_dev, Y_dev, mask_dev, X_test, Y_test, mask_test, \

embedd_table, label_alphabet, \

C_train, C_dev, C_test, char_embedd_table = data_processor.load_dataset_sequence_labeling(train_path, dev_path,

test_path, oov=oov,

fine_tune=fine_tune,

embedding=embedding,