kenlm init
This commit is contained in:
parent
1673116cf1
commit
2535098bfb
1
.gitignore
vendored
1
.gitignore
vendored
@ -8,3 +8,4 @@
|
||||
.token
|
||||
geval
|
||||
*in.tsv
|
||||
train_file.txt
|
10519
dev-0/out.tsv
10519
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
85
run.py
85
run.py
@ -1,80 +1,39 @@
|
||||
#%%
|
||||
import pandas as pd
|
||||
from collections import defaultdict, Counter
|
||||
|
||||
from sqlalchemy import true
|
||||
from nltk import trigrams, word_tokenize, bigrams
|
||||
import kenlm
|
||||
import csv
|
||||
|
||||
#%%
|
||||
class Model:
|
||||
def __init__(self):
|
||||
self.model = defaultdict(lambda: defaultdict(lambda: 0))
|
||||
self.model_bi = defaultdict(lambda: defaultdict(lambda: 0))
|
||||
train_in = pd.read_csv("train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)[[6, 7]]
|
||||
train_expected = pd.read_csv("train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)
|
||||
data = pd.concat([train_in, train_expected], axis=1)
|
||||
self.data = data[6] + data[0] + data[7]
|
||||
self.data = self.data.apply(self.clean)
|
||||
|
||||
|
||||
def clean(self, text):
|
||||
def clean(text):
|
||||
text = str(text).lower().strip().replace("’", "'").replace('\\n', " ").replace("'t", " not").replace("'s", " is").replace("'ll", " will").replace("'m", " am").replace("'ve", " have").replace(",", "").replace("-", "")
|
||||
return text
|
||||
|
||||
def train(self):
|
||||
alpha = 0.6
|
||||
vocab = set()
|
||||
for text in model.data:
|
||||
words = word_tokenize(text)
|
||||
for w1, w2, w3 in trigrams(words):
|
||||
self.model[w1, w2][w3] += 1
|
||||
vocab.add(w1)
|
||||
vocab.add(w2)
|
||||
vocab.add(w3)
|
||||
for w1, w2 in bigrams(words):
|
||||
self.model_bi[w1][w2] +=1
|
||||
for w1, w2 in self.model:
|
||||
total_count = float(sum(self.model[w1, w2].values()))
|
||||
for w in self.model[w1, w2]:
|
||||
self.model[w1, w2][w] = (self.model[w1, w2][w] / total_count) * alpha
|
||||
for w1 in self.model_bi:
|
||||
total_count = float(sum(self.model_bi[w1].values()))
|
||||
for w in self.model_bi[w1]:
|
||||
self.model_bi[w1][w] = (self.model_bi[w1][w] / total_count) * (1-alpha)
|
||||
train_in = pd.read_csv("train/in.tsv.xz", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)[[6, 7]]
|
||||
train_expected = pd.read_csv("train/expected.tsv", sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE, nrows=300000)
|
||||
data = pd.concat([train_in, train_expected], axis=1)
|
||||
data = data[6] + data[0] + data[7]
|
||||
data = data.apply(clean)
|
||||
|
||||
def predict(self, words):
|
||||
trigrams = Counter(dict(self.model[words]))
|
||||
bigrams = Counter(dict(self.model_bi[words[-1]]))
|
||||
predictions = dict((trigrams + bigrams).most_common(6))
|
||||
total_prob = 0
|
||||
|
||||
result = ""
|
||||
for word, prob in predictions.items():
|
||||
total_prob += prob
|
||||
result += f"{word}:{prob} "
|
||||
|
||||
if len(result) == 0:
|
||||
return "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1"
|
||||
return result + f":{max(1-total_prob, 0.01)}"
|
||||
|
||||
model = Model()
|
||||
with open("train_file.txt", "w+") as f:
|
||||
for text in data:
|
||||
f.write(text + "\n")
|
||||
|
||||
#%%
|
||||
model.data
|
||||
model.train()
|
||||
KENLM_BUILD_PATH='../kenlm/build'
|
||||
!$KENLM_BUILD_PATH/bin/lmplz -o 4 < train_file.txt > model.arpa
|
||||
!rm train_file.txt
|
||||
|
||||
|
||||
#%%
|
||||
def predict(model, path, result_path):
|
||||
model = kenlm.Model("model.arpa")
|
||||
#%%
|
||||
def predict(path, result_path):
|
||||
data = pd.read_csv(path, sep='\t', header=None, encoding="UTF-8", on_bad_lines="skip", quoting=csv.QUOTE_NONE)[7]
|
||||
with open(result_path, "w+", encoding="UTF-8") as f:
|
||||
for text in data:
|
||||
words = word_tokenize(model.clean(text))
|
||||
if len(words) < 2:
|
||||
prediction = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1"
|
||||
else:
|
||||
prediction = model.predict((words[-2], words[-1]))
|
||||
f.write(prediction + "\n")
|
||||
#test
|
||||
print(model.score(text, bos = True, eos = True))
|
||||
break
|
||||
|
||||
predict(model, "dev-0/in.tsv.xz", "dev-0/out.tsv")
|
||||
predict(model, "test-A/in.tsv.xz", "test-A/out.tsv")
|
||||
predict("dev-0/in.tsv.xz", "dev-0/out.tsv")
|
||||
predict("test-A/in.tsv.xz", "test-A/out.tsv")
|
7414
test-A/out.tsv
7414
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user