metadata
license: mit
Usage
import torch
from informer_models import InformerConfig, InformerForSequenceClassification
# Loading the model
model = InformerForSequenceClassification.from_pretrained("BrachioLab/supernova-classification")
model.to(device)
model.eval()
y_true = []
y_pred = []
for i, batch in enumerate(test_dataloader):
print(f"processing batch {i}")
batch = {k: v.to(device) for k, v in batch.items() if k != "objid"}
with torch.no_grad():
outputs = model(**batch)
y_true.extend(batch['labels'].cpu().numpy())
y_pred.extend(torch.argmax(outputs.logits, dim=2).squeeze().cpu().numpy())
print(f"accuracy: {sum([1 for i, j in zip(y_true, y_pred) if i == j]) / len(y_true)}")