challenging-america-word-ga.../predict.py

79 lines
2.0 KiB
Python

import kenlm
import csv
def predict_probability(sentence):
return model.score(sentence)
def load_candidate_words(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
candidate_words = {line.strip() for line in file}
return candidate_words
def predict_word_between(text1, text2, model, candidate_words):
max_prob = float("-inf")
best_word = None
for word in candidate_words:
sentence = f"{text1} {word} {text2}"
prob = model.score(sentence)
if prob > max_prob:
max_prob = prob
best_word = word
return best_word
dev = []
test = []
with open('dev-0/in_1.csv', 'r', newline='', encoding='utf-8') as file:
reader = csv.reader(file, delimiter=',')
for row in reader:
dev.append(row)
with open('test-A/in_1.csv', 'r', newline='', encoding='utf-8') as file:
reader = csv.reader(file, delimiter=',')
for row in reader:
test.append(row)
model_path = "model.binary"
model = kenlm.Model(model_path)
candidate_words_file = "words_3.txt"
candidate_words = load_candidate_words(candidate_words_file)
predicted_dev = []
predicted_test = []
i = 0
for row in dev:
text1 = row[0]
text2 = row[1]
predicted_word = predict_word_between(text1, text2, model, candidate_words)
predicted_dev.append(predicted_word)
if i % 500 == 0:
print(f'{i/len(dev)*100}%')
i += 1
with open('dev-0/out.tsv', 'w', newline='') as tsv_file:
tsv_writer = csv.writer(tsv_file, delimiter='\t')
for row in predicted_dev:
tsv_writer.writerow(row)
i = 0
for row in test:
text1 = row[0]
text2 = row[1]
predicted_word = predict_word_between(text1, text2, model, candidate_words)
predicted_test.append(predicted_word)
if i % 500 == 0:
print(f'{i/len(dev)*100}%')
i += 1
with open('test-A/out.tsv', 'w', newline='') as tsv_file:
tsv_writer = csv.writer(tsv_file, delimiter='\t')
for row in predicted_test:
tsv_writer.writerow(row)