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