Added uncertainty estimation scripts for ByT5 architecture models #63
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HaukurPall
approved these changes
Nov 13, 2023
| model = model.train() | ||
| else: | ||
| model = model.eval() | ||
| model = model.to("cuda").half() if use_gpu else model |
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Er half ok? Þjálfum við með FP16? Ég held að við gerum það en ég veit að upphaflegu líkönin eru með Bfloat16
| MAX_LENGTH = 512 | ||
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| def batch_by_n(iterable, batch_size): |
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þetta fall er skilgreint í hinni skránni líka. Import-a?
| model = T5ForConditionalGeneration.from_pretrained(str(checkpoint_path)) | ||
| model = model.to("cuda").half() if use_gpu else model | ||
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| def prepare_model_inputs(data_lines): |
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Þetta er líka skilgreint í hinni skránni og mikið af þessum kóða. Er hægt að samnýta?
| permuted_stds.append(seq_stds) | ||
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| # # recover the original order by inverting the length-sorted indices | ||
| inverse_indices = sorted_indices.argsort().tolist() |
| # we need to pass the attention mask to the model (encoder attention mask) | ||
| encoder_attention_mask = model_inputs["attention_mask"] | ||
| tgt_ids = prepare_model_inputs(decoded_output)["input_ids"] | ||
| cpu = torch.device("cpu") |
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ég vona að þetta sé ekki dýrt kall
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