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challenging-america-word-gap-prediction
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kenlm solution

This commit is contained in:
Jakub Eichner 2023-04-26 08:23:58 +02:00
parent 64b2612ef1
commit 975dd50258
3 changed files with 4 additions and 2075 deletions
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1988
kenlm.ipynb
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79
kenlm_2words.py
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@ -1,79 +0,0 @@
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, w3):
best_scores = []
pred_str = ""
# for word in get_english_words_set(['web2'], lower=True):
for word in V_counter:
text = ' '.join([w1, word, w3])
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 = w1 + ' ' + word + ' ' + w2
text_score = model.score(sentence, bos=False, eos=False)
yield((word, text_score))
def argmax(w1,w2):
# get top 10 predictions from predict_line
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):
print(f"Run predictions on {source_folder} data...")
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:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
else:
w1 = word_tokenize(l1)[-1]
w2 = word_tokenize(l2)[0]
out_line = predict_probs(w1, w2)
output_file.write(out_line + "\n")
run_predictions("dev-0")
run_predictions("test-A")

12
kenlm_4words.py → kenlm_run.py
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@ -23,7 +23,6 @@ def clean_string(text):
def predict_probs(w1, w2, w4, w5):
best_scores = []
pred_str = ""
# for word in get_english_words_set(['web2'], lower=True):
for word in V_counter:
text = ' '.join([w1, w2, word, w4, w5])
text_score = model.score(text, bos=False, eos=False)
@ -42,19 +41,16 @@ def predict_probs(w1, w2, w4, w5):
def get_word_predictions(w1, w2,):
for word in get_english_words_set(['web2'], lower=True):
sentence = w1 + ' ' + word + ' ' + w2
text_score = model.score(sentence, bos=False, eos=False)
sentence = f'{w1} {word} {w2}'
text_score = model.score(sentence, False, False)
yield((word, text_score))
def argmax(w1,w2):
# get top 10 predictions from predict_line
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):
print(f"Run predictions on {source_folder} data...")
def run_predictions(source_folder):
with open(f"{source_folder}/in.tsv", encoding="utf8", mode="rt") as file:
train_data = file.readlines()
@ -66,7 +62,7 @@ def run_predictions(source_folder):
l2 = clean_string(line[-1])
if not l1 or not l2:
out_line = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
out_line = "the:0.5 a:0.3 :0.2"
else:
w1, w2 = word_tokenize(l1)[-2:]
w3, w4 = word_tokenize(l2)[:2]