This commit is contained in:
Marcin Kostrzewski 2023-07-24 22:21:24 +02:00
parent eb63850925
commit af0c257a0e
8 changed files with 54089 additions and 17933 deletions

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dev-0/out-hidden_size=128.tsv Normal file

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dev-0/out-hidden_size=512.tsv Normal file

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gonito.yml Normal file
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description: self-made trigram model with center context
tags:
- neural-network
- n-gram
- self-made
params:
embedding_size: 256
lr: 0.00007
hidden_size: 128
epochs: 5
vocab_size: 16384
links:
- title: "repository"
url: "https://git.wmi.amu.edu.pl/s444501/challenging-america-word-gap-prediction.git"

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run-8_1.py Normal file
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
get_ipython().system('git clone --single-branch git://gonito.net/challenging-america-word-gap-prediction -b master')
# In[2]:
from torch import device as dev
device = dev("cuda")
# In[3]:
import lzma
def read_xz_file(fname):
with lzma.open(fname, mode='rt', encoding='utf-8') as f:
return [line.strip() for line in f.readlines()]
def read_file(fname):
with open(fname, mode='rt', encoding='utf-8') as f:
return [line.strip() for line in f.readlines()]
def get_contexts(input_text):
all_fields = input_text.replace(r'\n', ' ').split('\t')
return {'left': all_fields[6], 'right': all_fields[7]}
def compose_sentences(raw_input, labels):
result = []
for input, label in zip(raw_input, labels):
context = get_contexts(input)
result.append(f'{context["left"]} {input} {context["right"]}')
return result
# In[4]:
train_input_raw = read_xz_file('challenging-america-word-gap-prediction/train/in.tsv.xz')
train_labels = read_file('challenging-america-word-gap-prediction/train/expected.tsv')
train_sentences = compose_sentences(train_input_raw, train_labels)
# In[5]:
from torchtext.data import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch import save as save_model
def tokenize_dataset(lines, tokenizer):
for line in lines:
yield tokenizer(line)
vocabulary_max_size = 16384
unknown_token = '<0>'
tokenizer = get_tokenizer('basic_english')
vocabulary = build_vocab_from_iterator(
tokenize_dataset(train_sentences, tokenizer),
specials=[unknown_token],
max_tokens=vocabulary_max_size
)
vocabulary.set_default_index(vocabulary[unknown_token])
save_model(vocabulary, 'vocabulary.pth')
# In[6]:
from torch import LongTensor
class TrigramDataset:
def __init__(self, lines, vocab, tokenizer, unknown_token):
self.unknown_token = unknown_token
self.vocab = vocab
self.tokenizer = tokenizer
self.lines = lines
def __getitem__(self, idx):
x = []
y = []
sentence = [self.vocab[token] for token in self.tokenizer(self.lines[idx])]
for pos, _ in enumerate(sentence):
prev = sentence[pos-1] if pos > 0 else self.vocab[self.unknown_token]
current = sentence[pos]
next = sentence[pos+1] if pos < len(sentence) - 1 else self.vocab[self.unknown_token]
x.append([prev, next])
y.append([current])
return LongTensor(x), LongTensor(y)
def __len__(self):
return len(self.lines)
# In[7]:
train_dataset = TrigramDataset(train_sentences, vocabulary, tokenizer, unknown_token)
# In[8]:
from torch import nn
class LanguageModel(nn.Module):
grams_count = 3
def __init__(self, vocabulary_size, embedding_size, hidden_size):
super(LanguageModel, self).__init__()
self.embedding_size = embedding_size
self.embedding = nn.Embedding(vocabulary_size, embedding_size)
self.layers = nn.Sequential(
nn.Linear((self.grams_count - 1) * embedding_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, vocabulary_size),
nn.Softmax(dim=1)
)
def forward(self, x):
x = self.embedding(x).view((-1, (self.grams_count - 1) * self.embedding_size))
return self.layers(x)
# In[12]:
from torch.optim import Adam
from torch import log
from tqdm import tqdm
def train(model, dataset, output_file, epochs):
optimizer = Adam(model.parameters(), lr=0.00007)
criterion = nn.NLLLoss()
model.to(device)
model.train()
for epoch in range(epochs):
for i, (x, y) in(bar := tqdm(enumerate(dataset), total=len(dataset))):
x = x.to(device)
y = y.to(device)
optimizer.zero_grad()
ypredicted = model(x)
loss = criterion(log(ypredicted), y[:,0])
if not i % 100:
bar.set_description(f'Epoch: {epoch}, Loss: {loss}, Batch: {i}')
loss.backward()
try:
nn.utils.clip_grad_norm_(model.parameters(), 5, error_if_nonfinite=True)
optimizer.step()
except RuntimeError:
print("Grad overflow")
save_model(model.state_dict(), output_file)
# In[13]:
embedding_size = 256
model = LanguageModel(len(vocabulary), embedding_size, 128)
# In[ ]:
train(model, train_dataset, 'test_model', 5)
# In[28]:
embedding_size = 256
model_512 = LanguageModel(len(vocabulary), embedding_size, 512)
train(model_512, train_dataset, 'test_model_512', 5)
# In[ ]:
embedding_size = 256
model = LanguageModel(len(vocabulary), embedding_size, 128)
# In[18]:
dev_input_raw = read_xz_file('challenging-america-word-gap-prediction/dev-0/in.tsv.xz')
dev_contexts = [get_contexts(t) for t in dev_input_raw]
test_input_raw = read_xz_file('challenging-america-word-gap-prediction/test-A/in.tsv.xz')
test_sentences = [get_contexts(t) for t in test_input_raw]
# In[15]:
from torch import load as load_model
vocabulary = load_model('vocabulary.pth')
tokenizer = get_tokenizer('basic_english')
#model = load_model('test_model')
model.eval()
# In[16]:
from torch import LongTensor, topk, log
from tqdm import tqdm
def predict_words(dataset, tokenizer, vocab, model):
preds = []
for entry in tqdm(dataset):
tokenized_left = tokenizer(entry['left'])
tokenized_right = tokenizer(entry['right'])
# [last word from left context, y, first word from right context]
src = LongTensor([vocab[tokenized_left[-1]], vocab[tokenized_right[0]]]).to(device)
output = model(src)
top = topk(output[0], 50)
probs, tokens = top.values.tolist(), vocab.lookup_tokens(top.indices.tolist())
current_output = ''
accumulated_probability = 0
for prob, token in zip(probs, tokens):
accumulated_probability += prob
current_output += f'{token.strip()}:{prob} '
current_output += f':{1 - accumulated_probability}'
preds.append(current_output)
return preds
# In[24]:
preds = predict_words(dev_contexts, tokenizer, vocabulary, model)
# In[25]:
with open('challenging-america-word-gap-prediction/dev-0/out-hidden_size=128.tsv', 'w') as f:
f.writelines(line + '\n' for line in preds)
# In[26]:
test_preds = predict_words(test_sentences, tokenizer, vocabulary, model)
with open('challenging-america-word-gap-prediction/test-A/out-hidden_size=128.tsv', 'w') as f:
f.writelines(line + '\n' for line in test_preds)
# In[29]:
#model_512 = load_model('test_model_512')
model_512.eval()
preds_512 = predict_words(dev_contexts, tokenizer, vocabulary, model_512)
with open('challenging-america-word-gap-prediction/dev-0/out-hidden_size=512.tsv', 'w') as f:
f.writelines(line + '\n' for line in preds)
test_preds_512 = predict_words(test_sentences, tokenizer, vocabulary, model_512)
with open('challenging-america-word-gap-prediction/test-A/out-hidden_size=512.tsv', 'w') as f:
f.writelines(line + '\n' for line in test_preds)

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