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license: mit |
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--- |
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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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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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``` |
