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--- |
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license: apache-2.0 |
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--- |
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Number of Epochs = 5 <br> |
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Dataset Size = 5.5 k samples [train/validation] <br> |
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Number of labels used = 2 <br> |
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Thresholding = True<br> |
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Thresholding value = 0.7<br> |
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Below is the function to aplly thresholding to output logits. |
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```python |
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def get_prediction(text): |
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encoding = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128) |
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encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()} |
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outputs = model(**encoding) |
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logits = outputs.logits |
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sigmoid = torch.nn.Sigmoid() |
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probs = sigmoid(logits.squeeze().cpu()) |
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probs = probs.detach().numpy() |
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label = np.argmax(probs, axis=-1) |
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if label == 1: |
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if probs[1] > 0.7: |
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return 1 |
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else: |
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return 0 |
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else: |
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return 0 |
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``` |
