File size: 6,948 Bytes
31f1c86 7511825 31f1c86 7511825 31f1c86 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import torch
import logging
from transformers import BertModel, BertTokenizerFast
import os
from pathlib import Path
import pandas as pd
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
CURRENT_PATH = Path(__file__).parent
def tokenize(dataset):
BERT_MAX_LEN = 512
tokenizer = BertTokenizerFast.from_pretrained(
"neuralmind/bert-base-portuguese-cased", max_length=BERT_MAX_LEN)
dataset = dataset.map(lambda example: tokenizer(
example["text"], truncation=True, padding="max_length", max_length=BERT_MAX_LEN))
return dataset
def create_dataloader(dataset, shuffle=True):
return DataLoader(dataset, batch_size=8, shuffle=shuffle, num_workers=8, drop_last=True)
class AutoEncoder(torch.nn.Module):
def __init__(self):
super().__init__()
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.bert = BertModel.from_pretrained(
'neuralmind/bert-base-portuguese-cased').to(self.device)
# Freeze BERT
for param in self.bert.parameters():
param.requires_grad = False
self.encoder = torch.nn.Sequential(
torch.nn.Linear(self.bert.config.hidden_size,
self.bert.config.hidden_size // 5),
torch.nn.ReLU(),
torch.nn.Linear(self.bert.config.hidden_size // 5,
self.bert.config.hidden_size // 10),
torch.nn.ReLU(),
torch.nn.Linear(self.bert.config.hidden_size // 10,
self.bert.config.hidden_size // 30),
torch.nn.ReLU(),
).to(self.device)
self.decoder = torch.nn.Sequential(
torch.nn.Linear(self.bert.config.hidden_size // 30,
self.bert.config.hidden_size // 10),
torch.nn.ReLU(),
torch.nn.Linear(self.bert.config.hidden_size // 10,
self.bert.config.hidden_size // 5),
torch.nn.ReLU(),
torch.nn.Linear(self.bert.config.hidden_size //
5, self.bert.config.hidden_size),
torch.nn.Sigmoid()
).to(self.device)
def forward(self, input_ids, attention_mask):
bert_output = self.bert(input_ids=input_ids,
attention_mask=attention_mask).last_hidden_state[:, 0, :]
encoded = self.encoder(bert_output)
decoded = self.decoder(encoded)
return bert_output, decoded
def load_models():
models = []
for domain in ['politics', 'news', 'law', 'social_media', 'literature', 'web']:
logging.info(f"Loading {domain} model...")
accumulator = []
for lang in ['brazilian', 'european']:
model = AutoEncoder()
model.load_state_dict(torch.load(os.path.join(
CURRENT_PATH, 'models', 'autoencoder', f'{domain}_{lang}_model.pt')))
accumulator.append(model)
models.append({
'models': accumulator,
'train_domain': domain,
})
return models
def benchmark(model, debug=False):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
df_results = pd.DataFrame(
columns=['train_domain', 'test_domain', 'accuracy', 'f1', 'precision', 'recall'])
train_domain = model['train_domain']
brazilian_model = model['models'][0]
european_model = model['models'][1]
brazilian_model.eval()
european_model.eval()
brazilian_model.to(device)
european_model.to(device)
for test_domain in ['politics', 'news', 'law', 'social_media', 'literature', 'web']:
dataset = load_dataset(
'arubenruben/Portuguese_Language_Identification', test_domain, split='test')
if debug:
logging.info(f"Debugging {test_domain} dataset...")
dataset = dataset.select(range(100))
else:
dataset = dataset.shuffle().select(range(min(50_000, len(dataset))))
dataset = tokenize(dataset)
dataset.set_format(type='torch', columns=[
'input_ids', 'attention_mask', 'label'])
dataset = create_dataloader(dataset)
predictions = []
labels = []
reconstruction_loss = torch.nn.MSELoss(reduction='none')
with torch.no_grad():
for batch in tqdm(dataset):
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
label = batch['label'].to(device)
bert_european, reconstruction_european = european_model(
input_ids=input_ids, attention_mask=attention_mask)
bert_brazilian, reconstruction_brazilian = brazilian_model(
input_ids=input_ids, attention_mask=attention_mask)
test_loss_european = reconstruction_loss(
reconstruction_european, bert_european)
test_loss_brazilian = reconstruction_loss(
reconstruction_brazilian, bert_brazilian)
for loss_european, loss_brazilian in zip(test_loss_european, test_loss_brazilian):
if loss_european.mean().item() < loss_brazilian.mean().item():
predictions.append(0)
total_loss += loss_european.mean().item() / len(test_loss_european)
else:
predictions.append(1)
total_loss += loss_brazilian.mean().item() / len(test_loss_brazilian)
labels.extend(label.tolist())
accuracy = accuracy.compute(
predictions=predictions, references=labels)['accuracy']
f1 = f1.compute(predictions=predictions, references=labels)['f1']
precision = precision.compute(
predictions=predictions, references=labels)['precision']
recall = recall.compute(predictions=predictions,
references=labels)['recall']
df_results = pd.concat([df_results, pd.DataFrame(
[[train_domain, test_domain, accuracy, f1, precision, recall]], columns=df_results.columns)], ignore_index=True)
return df_results
def test():
DEBUG = True
models = load_models()
df_results = pd.DataFrame(
columns=['train_domain', 'test_domain', 'accuracy', 'f1', 'precision', 'recall'])
for model in models:
logging.info(f"Train Domain {model['train_domain']}...")
df_results = pd.concat([df_results, benchmark(
model, debug=DEBUG)], ignore_index=True)
logging.info(f"Saving results...")
df_results.to_json(os.path.join(CURRENT_PATH, 'results',
'autoencoder.json'), orient='records', indent=4, force_ascii=False)
if __name__ == '__main__':
test()
|