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--- |
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library_name: transformers |
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tags: [] |
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--- |
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```py |
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def scaling(x, min_x, max_x, r1, r2): |
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# Scale data x (n_samples x 1) to [r1, r2] |
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x_s = x |
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x_s = (x_s - min_x) * (r2 - r1) / (max_x - min_x) |
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x_s = r1 + x_s |
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return x_s |
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def descaling(x_s, min_x, max_x, r1, r2): |
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# Re-scale data x (n_samples x 1) to [min_x, max_x] |
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x = x_s |
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x = (x - r1) * (max_x - min_x) / (r2 - r1) + min_x |
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return x |
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# Inference example |
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with torch.no_grad(): |
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x = "They are equally important, absolutely, and just as real as each other." |
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x = tokenizer([x], return_tensors="pt", add_special_tokens=True, padding=True) |
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y_hat = model(**x.to(device)).logits |
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y_hat = torch.tanh(y_hat).cpu() |
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l_hat = descaling(y_hat, 1, 7, -1, 1)[0].numpy() |
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print(l_hat) |
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# [C, O, E, A, S] |
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# [6.0583944 4.4941516 1.6538751 5.5261126 4.725995 ] |
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``` |
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