def load_model(options):
print("Model path:",options.params)
with open(options.params, 'r') as paramsfp:
words, w2i, c2i, pos, rels, stored_opt = pickle.load(paramsfp)
stored_opt.external_embedding = None
print 'Loading pre-trained model'
parser = learner.jPosDepLearner(words, pos, rels, w2i, c2i, stored_opt)
parser.Load(options.model)
sys.setrecursionlimit(10000)
return parser
dest="costaugFlag",
default=True)
parser.add_option("--dynet-seed", type="int", dest="seed", default=0)
parser.add_option("--dynet-mem", type="int", dest="mem", default=0)
(options, args) = parser.parse_args()
#print 'Using external embedding:', options.external_embedding
if options.predictFlag:
with open(options.params, 'r') as paramsfp:
words, w2i, c2i, pos, rels, caps, stored_opt = pickle.load(
paramsfp)
stored_opt.external_embedding = None
print 'Loading pre-trained model'
parser = learner.jPosDepLearner(words, pos, rels, w2i, c2i, caps,
stored_opt)
parser.Load(options.model)
testoutpath = os.path.join(options.output, options.conll_test_output)
print 'Predicting POS tags and parsing dependencies'
#ts = time.time()
#test_pred = list(parser.Predict(options.conll_test))
#te = time.time()
#print 'Finished in', te-ts, 'seconds.'
#utils.write_conll(testoutpath, test_pred)
with open(testoutpath, 'w') as fh:
for sentence in parser.Predict(options.conll_test):
print sentence
for entry in sentence[1:]:
fh.write(str(entry) + '\n')
(options, args) = parser.parse_args()
#print 'Using external embedding:', options.external_embedding
pretrained_flag = False
if options.predictFlag:
print("PREDICT...")
with open(os.path.join(options.output, options.params),
'rb') as paramsfp:
words, w2i, c2i, m2i, t2i, morph_dict, pos, rels, stored_opt = pickle.load(
paramsfp)
stored_opt.external_embedding = None
print('Loading pre-trained model')
parser = learner.jPosDepLearner(words, pos, rels, w2i, c2i, m2i, t2i,
morph_dict, stored_opt)
parser.Load(os.path.join(options.output, options.model))
testoutpath = os.path.join(options.output, options.conll_test_output)
print('Predicting POS tags and parsing dependencies')
with open(testoutpath, 'w') as fh:
for sentence in parser.Predict(options.conll_test):
for entry in sentence[1:]:
fh.write(str(entry) + '\n')
fh.write('\n')
else:
print("Training file: " + options.conll_train)
highestScore = 0.0
eId = 0
