171 KiB
171 KiB
import torch
from torch import nn
torch.cuda.empty_cache()import pandas as pd
import regex as re
import csv
def clean_text(text):
text = text.lower().replace('-\\\\\\\\n', '').replace('\\\\\\\\n', ' ')
text = re.sub(r'\p{P}', '', text)
text = text.replace("'t", " not").replace("'s", " is").replace("'ll", " will").replace("'m", " am").replace("'ve", " have")
return texttrain_data = pd.read_csv('train/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
train_labels = pd.read_csv('train/expected.tsv', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
train_data = train_data[[6, 7]]
train_data = pd.concat([train_data, train_labels], axis=1)
train_data['text'] = train_data[6] + train_data[0] + train_data[7]
train_data = train_data[['text']]
with open('processed_train.txt', 'w', encoding='utf-8') as file:
for _, row in train_data.iterrows():
text = clean_text(str(row['text']))
file.write(text + '\n')vocab_size = 40000
embed_size = 300
hidden_size = 128
class SimpleTrigramNeuralLanguageModel(nn.Module):
def __init__(self, vocabulary_size, embedding_size, hidden_size):
super(SimpleTrigramNeuralLanguageModel, self).__init__()
self.embedding = nn.Embedding(vocabulary_size * 2, embedding_size)
self.linear1 = nn.Linear(embedding_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, vocabulary_size * 2)
def forward(self, x):
x = self.embedding(x)
x = self.linear1(x)
x = self.linear2(x)
x = torch.softmax(x, dim=1)
return ximport regex as re
from itertools import islice, chain
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import IterableDataset
def get_words_from_line(line):
line = line.rstrip()
yield '<s>'
for m in re.finditer(r'[\p{L}0-9\*]+|\p{P}+', line):
yield m.group(0).lower()
yield '</s>'
def get_word_lines_from_file(file_name):
with open(file_name, 'r', encoding='utf-8') as fh:
for line in fh:
yield get_words_from_line(line)
def look_ahead_iterator(gen):
prev_1 = None
prev_2 = None
for item in gen:
if prev_1 and prev_2:
yield (prev_2 + prev_1, item)
prev_2 = prev_1
prev_1 = itemclass Trigrams(IterableDataset):
def __init__(self, text_file, vocabulary_size):
self.vocab = build_vocab_from_iterator(
get_word_lines_from_file(text_file),
max_tokens = vocabulary_size,
specials = ['<unk>']
)
self.vocab.set_default_index(self.vocab['<unk>'])
self.vocabulary_size = vocabulary_size
self.text_file = text_file
def __iter__(self):
return look_ahead_iterator((self.vocab[t] for t in chain.from_iterable(get_word_lines_from_file(self.text_file))))from torch.utils.data import DataLoader
device = 'cuda' if torch.cuda.is_available() else 'cpu'
train_dataset = Trigrams('processed_train.txt', vocab_size)
model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)
data = DataLoader(train_dataset, batch_size=800)
optimizer = torch.optim.Adam(model.parameters())
criterion = torch.nn.NLLLoss()step = 0
for epoch in range(2):
model.train()
for x, y in data:
x = x.to(device)
y = y.to(device)
optimizer.zero_grad()
outputs = model(x)
loss = criterion(torch.log(outputs), y)
if step % 100 == 0:
print(step, loss)
step += 1
loss.backward()
optimizer.step()0 tensor(11.3293, device='cuda:0', grad_fn=<NllLossBackward0>) 100 tensor(8.9417, device='cuda:0', grad_fn=<NllLossBackward0>) 200 tensor(7.0454, device='cuda:0', grad_fn=<NllLossBackward0>) 300 tensor(6.8511, device='cuda:0', grad_fn=<NllLossBackward0>) 400 tensor(6.8680, device='cuda:0', grad_fn=<NllLossBackward0>) 500 tensor(6.8153, device='cuda:0', grad_fn=<NllLossBackward0>) 600 tensor(6.5640, device='cuda:0', grad_fn=<NllLossBackward0>) 700 tensor(6.8175, device='cuda:0', grad_fn=<NllLossBackward0>) 800 tensor(6.6864, device='cuda:0', grad_fn=<NllLossBackward0>) 900 tensor(6.7530, device='cuda:0', grad_fn=<NllLossBackward0>) 1000 tensor(6.5542, device='cuda:0', grad_fn=<NllLossBackward0>) 1100 tensor(6.5068, device='cuda:0', grad_fn=<NllLossBackward0>) 1200 tensor(6.7081, device='cuda:0', grad_fn=<NllLossBackward0>) 1300 tensor(6.2363, device='cuda:0', grad_fn=<NllLossBackward0>) 1400 tensor(6.5277, device='cuda:0', grad_fn=<NllLossBackward0>) 1500 tensor(6.5607, 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torch.save(model.state_dict(), 'model/model1.bin')device = 'cuda'
model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)
model.load_state_dict(torch.load('model/model1.bin'))
model.eval()
def predict(words):
ixs = torch.tensor(train_dataset.vocab.forward(['with'])).to(device)
predictions = model(ixs)
top = torch.topk(out[0], 30)
top_indices = top.indices.tolist()
top_probs = top.values.tolist()
top_words = train_dataset.vocab.lookup_tokens(top_indices)
top_preds = list(zip(top_words, top_indices, top_probs))
total_prob = 0.0
pred_str = ''
for word, _, prob in top_preds:
if word != '<unk>':
pred_str += f'{word}:{prob} '
total_prob += prob
pred_str += f':{1 - total_prob}'
return pred_strdev_data = pd.read_csv('dev-0/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
test_data = pd.read_csv('test-A/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)from nltk import word_tokenize
with open('dev-0/out.tsv', 'w') as file:
for index, row in dev_data.iterrows():
left_text = clean_text(str(row[6]))
left_words = word_tokenize(left_text)
if len(left_words) < 3:
prediction = ':1.0'
else:
prediction = predict(left_words[-2:])
file.write(prediction + '\n')with open('test-A/out.tsv', 'w') as file:
for index, row in test_data.iterrows():
left_text = clean_text(str(row[6]))
left_words = word_tokenize(left_text)
if len(left_words) < 3:
prediction = ':1.0'
else:
prediction = predict(left_words[-2:])
file.write(prediction + '\n')