def __init__(self,
model_path,
word_dim=None,
afix_dim=None,
nlayers=2,
hidden_dim=128,
elu_dim=64,
dep_dim=100,
dropout_ratio=0.5):
self.model_path = model_path
defs_file = model_path + "/tagger_defs.txt"
if word_dim is None:
self.train = False
Param.load(self, defs_file)
self.extractor = FeatureExtractor(model_path)
else:
# training
self.train = True
p = Param(self)
p.dep_dim = dep_dim
p.word_dim = word_dim
p.afix_dim = afix_dim
p.hidden_dim = hidden_dim
p.elu_dim = elu_dim
p.nlayers = nlayers
p.n_words = len(read_model_defs(model_path + "/words.txt"))
p.n_suffixes = len(read_model_defs(model_path + "/suffixes.txt"))
p.n_prefixes = len(read_model_defs(model_path + "/prefixes.txt"))
p.targets = read_model_defs(model_path + "/target.txt")
p.dump(defs_file)
self.in_dim = self.word_dim + 8 * self.afix_dim
self.dropout_ratio = dropout_ratio
super(LSTMParser,
self).__init__(emb_word=L.EmbedID(self.n_words, self.word_dim),
emb_suf=L.EmbedID(self.n_suffixes,
self.afix_dim,
ignore_label=IGNORE),
emb_prf=L.EmbedID(self.n_prefixes,
self.afix_dim,
ignore_label=IGNORE),
lstm_f=L.NStepLSTM(nlayers, self.in_dim,
self.hidden_dim,
self.dropout_ratio),
lstm_b=L.NStepLSTM(nlayers, self.in_dim,
self.hidden_dim,
self.dropout_ratio),
linear_cat1=L.Linear(2 * self.hidden_dim,
self.elu_dim),
linear_cat2=L.Linear(self.elu_dim,
len(self.targets)),
linear_dep=L.Linear(2 * self.hidden_dim,
self.dep_dim),
linear_head=L.Linear(2 * self.hidden_dim,
self.dep_dim),
biaffine=Biaffine(self.dep_dim))
def __init__(self, model_path):
self.model_path = model_path
self.words = read_model_defs(model_path / 'words.txt')
self.chars = read_model_defs(model_path / 'chars.txt')
self.unk_word = self.words[UNK]
self.start_word = self.words[START]
self.end_word = self.words[END]
self.unk_char = self.chars[UNK]
self.start_char = self.chars[START]
self.end_char = self.chars[END]
def __init__(self, model_path):
self.words = read_model_defs(model_path + "/words.txt")
self.suffixes = read_model_defs(model_path + "/suffixes.txt")
self.prefixes = read_model_defs(model_path + "/prefixes.txt")
self.unk_word = self.words[UNK]
self.start_word = self.words[START]
self.end_word = self.words[END]
self.unk_suf = self.suffixes[UNK]
self.unk_prf = self.prefixes[UNK]
self.start_pre = [[self.prefixes[START]] + [-1] * 3]
self.start_suf = [[self.suffixes[START]] + [-1] * 3]
self.end_pre = [[self.prefixes[END]] + [-1] * 3]
self.end_suf = [[self.suffixes[END]] + [-1] * 3]
def __init__(self, model_path, length=False):
self.words = read_model_defs(model_path / 'words.txt')
self.suffixes = read_model_defs(model_path / 'suffixes.txt')
self.prefixes = read_model_defs(model_path / 'prefixes.txt')
self.unk_word = self.words[UNK]
self.start_word = self.words[START]
self.end_word = self.words[END]
self.unk_suf = self.suffixes[UNK]
self.unk_prf = self.prefixes[UNK]
self.start_pre = [[self.prefixes[START]] + [IGNORE] * 3]
self.start_suf = [[self.suffixes[START]] + [IGNORE] * 3]
self.end_pre = [[self.prefixes[END]] + [IGNORE] * 3]
self.end_suf = [[self.suffixes[END]] + [IGNORE] * 3]
self.length = length
def augment_pretrained_with_random_initialization(args):
words = OrderedDict()
# words in pretrained word embedding
for word in open(args.pretrained_vocab):
words[word.strip()] = 1
# words in specials e.g. PAD, START, END
for word in args.specials:
words[word] = 1
# words found in training data
for word, freq in read_model_defs(args.new_words).items():
if freq >= args.freq_cut:
words[word.encode("utf-8")] = freq
new_pretrained_vocab = os.path.join(args.out, "new_words.txt")
print >> sys.stderr, "writing to", new_pretrained_vocab
with open(new_pretrained_vocab, "w") as f:
for word, freq in words.items():
f.write("{} {}\n".format(word, freq))
embeddings = read_pretrained_embeddings(args.pretrained)
assert embeddings.shape[0] <= len(words), "pretrained size: {}, read words: {}".format(embeddings.shape[0], len(words))
new_embeddings = 0.02 * np.random.random_sample(
(len(words), embeddings.shape[1])).astype('f') - 0.01
for i in range(embeddings.shape[0]):
new_embeddings[i] = embeddings[i]
new_pretrained = os.path.join(args.out, "new_embeddings.txt")
print >> sys.stderr, "writing to", new_pretrained
np.savetxt(new_pretrained, new_embeddings)
print >> sys.stderr, "vocabulary size", len(embeddings), "-->", len(new_embeddings)
def __init__(self,
model_path,
word_dim=None,
afix_dim=None,
nlayers=2,
hidden_dim=128,
relu_dim=64,
dropout_ratio=0.5):
self.model_path = model_path
defs_file = model_path + "/tagger_defs.txt"
if word_dim is None:
self.train = False
Param.load(self, defs_file)
self.extractor = FeatureExtractor(model_path)
else:
self.train = True
p = Param(self)
p.word_dim = word_dim
p.afix_dim = afix_dim
p.hidden_dim = hidden_dim
p.relu_dim = relu_dim
p.nlayers = nlayers
p.dump(defs_file)
self.targets = read_model_defs(model_path + "/target.txt")
self.words = read_model_defs(model_path + "/words.txt")
self.suffixes = read_model_defs(model_path + "/suffixes.txt")
self.prefixes = read_model_defs(model_path + "/prefixes.txt")
self.in_dim = self.word_dim + 8 * self.afix_dim
self.dropout_ratio = dropout_ratio
super(LSTMTagger, self).__init__(
emb_word=L.EmbedID(len(self.words), self.word_dim),
emb_suf=L.EmbedID(len(self.suffixes),
self.afix_dim,
ignore_label=IGNORE),
emb_prf=L.EmbedID(len(self.prefixes),
self.afix_dim,
ignore_label=IGNORE),
lstm_f=L.NStepLSTM(nlayers, self.in_dim, self.hidden_dim, 0.),
lstm_b=L.NStepLSTM(nlayers, self.in_dim, self.hidden_dim, 0.),
linear1=L.Linear(2 * self.hidden_dim, self.relu_dim),
linear2=L.Linear(self.relu_dim, len(self.targets)),
)
def extract_subset_of_pretrained_embeddings(args):
embeddings = read_pretrained_embeddings(args.pretrained)
emb_words = [word.strip().decode("utf-8") for word in open(args.pretrained_vocab)]
subset = read_model_defs(args.new_words).keys()
new_pretrained = os.path.join(args.out, "extracted_embeddings.vector")
new_vocab = os.path.join(args.out, "extracted_embeddings.words")
print >> sys.stderr, "writing to", new_pretrained
with open(new_vocab, "w") as v:
with open(new_pretrained, "w") as f:
for i, word in enumerate(emb_words):
if word in subset:
f.write(" ".join([str(u) for u in embeddings[i]]) + "\n")
v.write(word.encode("utf-8") + "\n")
def __init__(self, model_path, ccgbank_path, tritrain_path, weight):
self.model_path = model_path
self.targets = read_model_defs(model_path + "/target.txt")
self.extractor = FeatureExtractor(model_path)
self.weight = weight
self.ncopies = 15
with open(ccgbank_path) as f:
self.ccgbank_samples = json.load(f)
self.ccgbank_size = len(self.ccgbank_samples)
with open(tritrain_path) as f:
self.tritrain_samples = json.load(f)
self.tritrain_size = len(self.tritrain_samples)
print >> sys.stderr, "len(ccgbank):", self.ccgbank_size
print >> sys.stderr, "len(ccgbank) * # copies:", self.ccgbank_size * self.ncopies
print >> sys.stderr, "len(tritrain):", self.tritrain_size
def __init__(self, model_path, samples_path):
self.model_path = model_path
self.targets = read_model_defs(model_path + "/target.txt")
self.extractor = FeatureExtractor(model_path)
with open(samples_path) as f:
self.samples = json.load(f).items()
def __init__(self,
model_path,
word_dim=None,
afix_dim=None,
nlayers=2,
hidden_dim=128,
elu_dim=64,
dep_dim=100,
dropout_ratio=0.5,
use_cudnn=False):
self.model_path = model_path
defs_file = model_path + "/tagger_defs.txt"
if word_dim is None:
self.train = False
Param.load(self, defs_file)
self.extractor = FeatureExtractor(model_path)
else:
self.train = True
p = Param(self)
p.dep_dim = dep_dim
p.word_dim = word_dim
p.afix_dim = afix_dim
p.hidden_dim = hidden_dim
p.elu_dim = elu_dim
p.nlayers = nlayers
p.n_words = len(read_model_defs(model_path + "/words.txt"))
p.n_suffixes = len(read_model_defs(model_path + "/suffixes.txt"))
p.n_prefixes = len(read_model_defs(model_path + "/prefixes.txt"))
p.targets = read_model_defs(model_path + "/target.txt")
p.dump(defs_file)
self.in_dim = self.word_dim + 8 * self.afix_dim
self.dropout_ratio = dropout_ratio
super(QRNNParser,
self).__init__(emb_word=L.EmbedID(self.n_words,
self.word_dim,
ignore_label=IGNORE),
emb_suf=L.EmbedID(self.n_suffixes,
self.afix_dim,
ignore_label=IGNORE),
emb_prf=L.EmbedID(self.n_prefixes,
self.afix_dim,
ignore_label=IGNORE),
qrnn_fs=ChainList(),
qrnn_bs=ChainList(),
arc_dep=L.Linear(2 * self.hidden_dim,
self.dep_dim),
arc_head=L.Linear(2 * self.hidden_dim,
self.dep_dim),
rel_dep=L.Linear(2 * self.hidden_dim,
self.dep_dim),
rel_head=L.Linear(2 * self.hidden_dim,
self.dep_dim),
biaffine_arc=Biaffine(self.dep_dim),
biaffine_tag=Bilinear(self.dep_dim, self.dep_dim,
len(self.targets)))
in_dim = self.in_dim
for _ in range(self.nlayers):
self.qrnn_fs.add_link(QRNNLayer(in_dim, self.hidden_dim))
self.qrnn_bs.add_link(QRNNLayer(in_dim, self.hidden_dim))
in_dim = self.hidden_dim
def __init__(self, model_path, samples_path):
self.model_path = model_path
self.targets = read_model_defs(model_path + "/target.txt")
self.extractor = FeatureExtractor(model_path)
with open(samples_path) as f:
self.samples = sorted(json.load(f), key=lambda x: len(x[1][0]))
