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d8289f0 34f8215 d8289f0 8dd9580 d8289f0 c898199 d8289f0 c898199 d8289f0 c898199 d8289f0 | 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 | import torch
from torch.utils.data import DataLoader, TensorDataset
class PopulationHealthScreener:
input_col = 'Abstract'
max_length = 512
batch_size = 8
def __init__(self, tokenizer, model):
self.tokenizer = tokenizer
self.model = model
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model.to(self.device)
def load_fine_tuned_weights(self, model_path):
state_dict = torch.load(model_path, map_location=self.device)
self.model.load_state_dict(state_dict, strict=False)
def load_test_data(self, data):
encodings = self.tokenizer(
data, truncation=True, padding='max_length',
max_length=self.max_length, return_tensors='pt'
)
dataset = TensorDataset(encodings['input_ids'], encodings['attention_mask'])
return DataLoader(dataset, batch_size=self.batch_size, shuffle=False)
def predict(self, data, on_batch=None):
test_data = self.load_test_data(data)
predictions = []
n_total = len(data)
self.model.eval()
for i, batch in enumerate(test_data):
input_ids = batch[0].to(self.device)
attention_mask = batch[1].to(self.device)
with torch.no_grad():
outputs = self.model(input_ids, attention_mask=attention_mask, output_hidden_states=True)
predictions.extend(torch.sigmoid(outputs.logits).flatten().tolist())
if on_batch:
on_batch(min((i + 1) * self.batch_size, n_total), n_total)
return predictions |