Compare commits
6 Commits
Author | SHA1 | Date | |
---|---|---|---|
![]() |
975dd50258 | ||
![]() |
64b2612ef1 | ||
![]() |
dfe830ac26 | ||
![]() |
20d03b9e18 | ||
![]() |
140f4c0284 | ||
![]() |
02ee0ff2fa |
2
.gitignore
vendored
2
.gitignore
vendored
@ -6,5 +6,3 @@
|
|||||||
*.o
|
*.o
|
||||||
.DS_Store
|
.DS_Store
|
||||||
.token
|
.token
|
||||||
.gin
|
|
||||||
geval
|
|
21038
dev-0/out.tsv
21038
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
75
kenlm_run.py
Normal file
75
kenlm_run.py
Normal file
@ -0,0 +1,75 @@
|
|||||||
|
from tqdm import tqdm
|
||||||
|
import regex as re
|
||||||
|
from nltk.tokenize import word_tokenize
|
||||||
|
from english_words import get_english_words_set
|
||||||
|
import kenlm
|
||||||
|
from math import log10
|
||||||
|
import pickle
|
||||||
|
|
||||||
|
path = 'kenlm_model.binary'
|
||||||
|
model = kenlm.Model(path)
|
||||||
|
|
||||||
|
with open('V.pickle', 'rb') as handle:
|
||||||
|
V_counter = pickle.load(handle)
|
||||||
|
|
||||||
|
def clean_string(text):
|
||||||
|
text = text.lower()
|
||||||
|
text = re.sub(r" -\\*\\n", "", text)
|
||||||
|
text = re.sub(r"\\n", " ", text)
|
||||||
|
text = text.strip()
|
||||||
|
return text
|
||||||
|
|
||||||
|
|
||||||
|
def predict_probs(w1, w2, w4, w5):
|
||||||
|
best_scores = []
|
||||||
|
pred_str = ""
|
||||||
|
for word in V_counter:
|
||||||
|
text = ' '.join([w1, w2, word, w4, w5])
|
||||||
|
text_score = model.score(text, bos=False, eos=False)
|
||||||
|
if len(best_scores) < 5:
|
||||||
|
best_scores.append((word, text_score))
|
||||||
|
else:
|
||||||
|
worst_score = best_scores[-1]
|
||||||
|
if worst_score[1] < text_score:
|
||||||
|
best_scores[-1] = (word, text_score)
|
||||||
|
best_scores = sorted(best_scores, key=lambda tup: tup[1], reverse=True)
|
||||||
|
|
||||||
|
for word, prob in best_scores:
|
||||||
|
pred_str += f'{word}:{prob} '
|
||||||
|
pred_str += f':{log10(0.99)}'
|
||||||
|
return pred_str
|
||||||
|
|
||||||
|
def get_word_predictions(w1, w2,):
|
||||||
|
for word in get_english_words_set(['web2'], lower=True):
|
||||||
|
sentence = f'{w1} {word} {w2}'
|
||||||
|
text_score = model.score(sentence, False, False)
|
||||||
|
yield((word, text_score))
|
||||||
|
|
||||||
|
def argmax(w1,w2):
|
||||||
|
top_10 = sorted(list(get_word_predictions(w1,w2)), key=lambda x: -x[1])[:4]
|
||||||
|
output_line = " ".join(["{}:{:.8f}".format(w, p) for w, p in top_10])
|
||||||
|
return output_line
|
||||||
|
|
||||||
|
def run_predictions(source_folder):
|
||||||
|
with open(f"{source_folder}/in.tsv", encoding="utf8", mode="rt") as file:
|
||||||
|
train_data = file.readlines()
|
||||||
|
|
||||||
|
with open(f"{source_folder}/out.tsv", "w", encoding="utf-8") as output_file:
|
||||||
|
for line in tqdm(train_data):
|
||||||
|
line = line.split("\t")
|
||||||
|
|
||||||
|
l1 = clean_string(line[-2])
|
||||||
|
l2 = clean_string(line[-1])
|
||||||
|
|
||||||
|
if not l1 or not l2:
|
||||||
|
out_line = "the:0.5 a:0.3 :0.2"
|
||||||
|
else:
|
||||||
|
w1, w2 = word_tokenize(l1)[-2:]
|
||||||
|
w3, w4 = word_tokenize(l2)[:2]
|
||||||
|
out_line = predict_probs(w1, w2, w3, w4)
|
||||||
|
|
||||||
|
output_file.write(out_line + "\n")
|
||||||
|
|
||||||
|
|
||||||
|
run_predictions("dev-0")
|
||||||
|
run_predictions("test-A")
|
7414
test-A/out.tsv
Normal file
7414
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user