forked from kentonl/e2e-coref
-
Notifications
You must be signed in to change notification settings - Fork 0
/
cache_elmo.py
55 lines (51 loc) · 2.05 KB
/
cache_elmo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import h5py
import json
import sys
def build_elmo():
token_ph = tf.placeholder(tf.string, [None, None])
len_ph = tf.placeholder(tf.int32, [None])
elmo_module = hub.Module("https://tfhub.dev/google/elmo/2")
lm_embeddings = elmo_module(
inputs={"tokens": token_ph, "sequence_len": len_ph},
signature="tokens", as_dict=True)
word_emb = lm_embeddings["word_emb"]
lm_emb = tf.stack([tf.concat([word_emb, word_emb], -1),
lm_embeddings["lstm_outputs1"],
lm_embeddings["lstm_outputs2"]], -1)
return token_ph, len_ph, lm_emb
def cache_dataset(data_path, session, token_ph, len_ph, lm_emb, out_file):
with open(data_path) as in_file:
for doc_num, line in enumerate(in_file.readlines()):
example = json.loads(line)
sentences = example["sentences"]
max_sentence_length = max(len(s) for s in sentences)
tokens = [[""] * max_sentence_length for _ in sentences]
text_len = np.array([len(s) for s in sentences])
for i, sentence in enumerate(sentences):
for j, word in enumerate(sentence):
tokens[i][j] = word
tokens = np.array(tokens)
tf_lm_emb = session.run(lm_emb, feed_dict={
token_ph: tokens,
len_ph: text_len
})
file_key = example["doc_key"].replace("/", ":")
group = out_file.create_group(file_key)
for i, (e, l) in enumerate(zip(tf_lm_emb, text_len)):
e = e[:l, :, :]
group[str(i)] = e
if doc_num % 10 == 0:
print("Cached {} documents in {}".format(doc_num + 1, data_path))
if __name__ == "__main__":
token_ph, len_ph, lm_emb = build_elmo()
with tf.Session() as session:
session.run(tf.global_variables_initializer())
with h5py.File("elmo_cache.hdf5", "w") as out_file:
for json_filename in sys.argv[1:]:
cache_dataset(json_filename, session, token_ph, len_ph, lm_emb, out_file)