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''' |
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Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved. |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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@File : utils.py |
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@Time : 2022/10/28 18:27 |
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@Author : Qi Yang |
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@Version : 1.0 |
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@Contact : [email protected] |
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@License : (C)Copyright 2022-2023, CCNL-IDEA |
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''' |
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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import torch |
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import torch.nn.functional as F |
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class LabelSmoothingCrossEntropy(torch.nn.Module): |
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def __init__(self, smoothing=0.1): |
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super(LabelSmoothingCrossEntropy, self).__init__() |
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self.smoothing = smoothing |
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self.ignore_index = -100 |
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def forward(self, x, target): |
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confidence = 1. - self.smoothing |
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logprobs = F.log_softmax(x, dim=-1) |
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targets_ignore = torch.where(target != self.ignore_index, target, 0) |
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nll_loss = -logprobs.gather(dim=-1, index=targets_ignore.unsqueeze(1)) |
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nll_loss = nll_loss.squeeze(1) |
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smooth_loss = -logprobs.mean(dim=-1) |
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loss = confidence * nll_loss + self.smoothing * smooth_loss |
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return loss.mean() |
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def truncate_sequence(document: str, max_num_tokens: int, reverse=False): |
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total_length = len(document) |
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if total_length <= max_num_tokens: |
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return document |
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else: |
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if reverse: |
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return document[-1*max_num_tokens:] |
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else: |
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return document[:max_num_tokens] |
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def padding_to_maxlength(ids, max_length, pad_id): |
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cur_len = len(ids) |
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len_diff = max_length - len(ids) |
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return ids + [pad_id] * len_diff, [1] * cur_len + [0] * len_diff |
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def white_space_fix(text): |
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return "".join(text.split(" ")) |
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def remove_prompt(text): |
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if ":" in text: |
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return text.split(":")[1] |
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return text |
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