translate internal folder files to chinese (#27638)
* translate * update * update --------- Co-authored-by: jiaqiw <wangjiaqi50@huawei.com>
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docs/source/zh/internal/generation_utils.md
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docs/source/zh/internal/generation_utils.md
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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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 distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# 用于生成的工具
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此页面列出了所有由 [`~generation.GenerationMixin.generate`],
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[`~generation.GenerationMixin.greedy_search`],
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[`~generation.GenerationMixin.contrastive_search`],
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[`~generation.GenerationMixin.sample`],
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[`~generation.GenerationMixin.beam_search`],
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[`~generation.GenerationMixin.beam_sample`],
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[`~generation.GenerationMixin.group_beam_search`], 和
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[`~generation.GenerationMixin.constrained_beam_search`]使用的实用函数。
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其中大多数仅在您研究库中生成方法的代码时才有用。
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## 生成输出
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[`~generation.GenerationMixin.generate`] 的输出是 [`~utils.ModelOutput`] 的一个子类的实例。这个输出是一种包含 [`~generation.GenerationMixin.generate`] 返回的所有信息数据结构，但也可以作为元组或字典使用。
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这里是一个例子：
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```python
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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model = GPT2LMHeadModel.from_pretrained("gpt2")
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inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
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generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
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```
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`generation_output` 的对象是 [`~generation.GreedySearchDecoderOnlyOutput`] 的一个实例，从该类的文档中我们可以看到，这意味着它具有以下属性：
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- `sequences`: 生成的tokens序列
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- `scores`（可选）: 每个生成步骤的语言建模头的预测分数
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- `hidden_states`（可选）: 每个生成步骤模型的hidden states
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- `attentions`（可选）: 每个生成步骤模型的注意力权重
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在这里，由于我们传递了 `output_scores=True`，我们具有 `scores` 属性。但我们没有 `hidden_states` 和 `attentions`，因为没有传递 `output_hidden_states=True` 或 `output_attentions=True`。
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您可以像通常一样访问每个属性，如果该属性未被模型返回，则将获得 `None`。例如，在这里 `generation_output.scores` 是语言建模头的所有生成预测分数，而 `generation_output.attentions` 为 `None`。
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当我们将 `generation_output` 对象用作元组时，它只保留非 `None` 值的属性。例如，在这里它有两个元素，`loss` 然后是 `logits`，所以
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```python
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generation_output[:2]
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```
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将返回元组`(generation_output.sequences, generation_output.scores)`。
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当我们将`generation_output`对象用作字典时，它只保留非`None`的属性。例如，它有两个键，分别是`sequences`和`scores`。
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我们在此记录所有输出类型。
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### PyTorch
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[[autodoc]] generation.GreedySearchEncoderDecoderOutput
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[[autodoc]] generation.GreedySearchDecoderOnlyOutput
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[[autodoc]] generation.SampleEncoderDecoderOutput
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[[autodoc]] generation.SampleDecoderOnlyOutput
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[[autodoc]] generation.BeamSearchEncoderDecoderOutput
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[[autodoc]] generation.BeamSearchDecoderOnlyOutput
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[[autodoc]] generation.BeamSampleEncoderDecoderOutput
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[[autodoc]] generation.BeamSampleDecoderOnlyOutput
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[[autodoc]] generation.ContrastiveSearchEncoderDecoderOutput
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[[autodoc]] generation.ContrastiveSearchDecoderOnlyOutput
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### TensorFlow
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[[autodoc]] generation.TFGreedySearchEncoderDecoderOutput
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[[autodoc]] generation.TFGreedySearchDecoderOnlyOutput
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[[autodoc]] generation.TFSampleEncoderDecoderOutput
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[[autodoc]] generation.TFSampleDecoderOnlyOutput
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[[autodoc]] generation.TFBeamSearchEncoderDecoderOutput
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[[autodoc]] generation.TFBeamSearchDecoderOnlyOutput
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[[autodoc]] generation.TFBeamSampleEncoderDecoderOutput
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[[autodoc]] generation.TFBeamSampleDecoderOnlyOutput
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[[autodoc]] generation.TFContrastiveSearchEncoderDecoderOutput
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[[autodoc]] generation.TFContrastiveSearchDecoderOnlyOutput
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### FLAX
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[[autodoc]] generation.FlaxSampleOutput
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[[autodoc]] generation.FlaxGreedySearchOutput
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[[autodoc]] generation.FlaxBeamSearchOutput
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## LogitsProcessor
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[`LogitsProcessor`] 可以用于修改语言模型头的预测分数以进行生成
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### PyTorch
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[[autodoc]] AlternatingCodebooksLogitsProcessor
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- __call__
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[[autodoc]] ClassifierFreeGuidanceLogitsProcessor
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- __call__
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[[autodoc]] EncoderNoRepeatNGramLogitsProcessor
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- __call__
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[[autodoc]] EncoderRepetitionPenaltyLogitsProcessor
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- __call__
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[[autodoc]] EpsilonLogitsWarper
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- __call__
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[[autodoc]] EtaLogitsWarper
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- __call__
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[[autodoc]] ExponentialDecayLengthPenalty
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- __call__
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[[autodoc]] ForcedBOSTokenLogitsProcessor
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- __call__
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[[autodoc]] ForcedEOSTokenLogitsProcessor
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- __call__
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[[autodoc]] ForceTokensLogitsProcessor
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- __call__
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[[autodoc]] HammingDiversityLogitsProcessor
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- __call__
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[[autodoc]] InfNanRemoveLogitsProcessor
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- __call__
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[[autodoc]] LogitNormalization
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- __call__
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[[autodoc]] LogitsProcessor
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- __call__
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[[autodoc]] LogitsProcessorList
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- __call__
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[[autodoc]] LogitsWarper
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- __call__
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[[autodoc]] MinLengthLogitsProcessor
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- __call__
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[[autodoc]] MinNewTokensLengthLogitsProcessor
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- __call__
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[[autodoc]] NoBadWordsLogitsProcessor
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- __call__
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[[autodoc]] NoRepeatNGramLogitsProcessor
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- __call__
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[[autodoc]] PrefixConstrainedLogitsProcessor
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- __call__
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[[autodoc]] RepetitionPenaltyLogitsProcessor
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- __call__
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[[autodoc]] SequenceBiasLogitsProcessor
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- __call__
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[[autodoc]] SuppressTokensAtBeginLogitsProcessor
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- __call__
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[[autodoc]] SuppressTokensLogitsProcessor
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- __call__
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[[autodoc]] TemperatureLogitsWarper
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- __call__
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[[autodoc]] TopKLogitsWarper
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- __call__
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[[autodoc]] TopPLogitsWarper
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- __call__
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[[autodoc]] TypicalLogitsWarper
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- __call__
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[[autodoc]] UnbatchedClassifierFreeGuidanceLogitsProcessor
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- __call__
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[[autodoc]] WhisperTimeStampLogitsProcessor
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- __call__
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### TensorFlow
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[[autodoc]] TFForcedBOSTokenLogitsProcessor
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- __call__
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[[autodoc]] TFForcedEOSTokenLogitsProcessor
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- __call__
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[[autodoc]] TFForceTokensLogitsProcessor
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- __call__
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[[autodoc]] TFLogitsProcessor
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- __call__
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[[autodoc]] TFLogitsProcessorList
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- __call__
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[[autodoc]] TFLogitsWarper
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- __call__
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[[autodoc]] TFMinLengthLogitsProcessor
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- __call__
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[[autodoc]] TFNoBadWordsLogitsProcessor
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- __call__
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[[autodoc]] TFNoRepeatNGramLogitsProcessor
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- __call__
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[[autodoc]] TFRepetitionPenaltyLogitsProcessor
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- __call__
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[[autodoc]] TFSuppressTokensAtBeginLogitsProcessor
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- __call__
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[[autodoc]] TFSuppressTokensLogitsProcessor
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- __call__
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[[autodoc]] TFTemperatureLogitsWarper
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- __call__
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[[autodoc]] TFTopKLogitsWarper
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- __call__
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[[autodoc]] TFTopPLogitsWarper
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- __call__
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### FLAX
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[[autodoc]] FlaxForcedBOSTokenLogitsProcessor
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- __call__
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[[autodoc]] FlaxForcedEOSTokenLogitsProcessor
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- __call__
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[[autodoc]] FlaxForceTokensLogitsProcessor
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- __call__
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[[autodoc]] FlaxLogitsProcessor
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- __call__
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[[autodoc]] FlaxLogitsProcessorList
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- __call__
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[[autodoc]] FlaxLogitsWarper
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- __call__
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[[autodoc]] FlaxMinLengthLogitsProcessor
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- __call__
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[[autodoc]] FlaxSuppressTokensAtBeginLogitsProcessor
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- __call__
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[[autodoc]] FlaxSuppressTokensLogitsProcessor
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- __call__
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[[autodoc]] FlaxTemperatureLogitsWarper
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- __call__
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[[autodoc]] FlaxTopKLogitsWarper
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- __call__
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[[autodoc]] FlaxTopPLogitsWarper
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- __call__
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[[autodoc]] FlaxWhisperTimeStampLogitsProcessor
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- __call__
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## StoppingCriteria
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可以使用[`StoppingCriteria`]来更改停止生成的时间（除了EOS token以外的方法）。请注意，这仅适用于我们的PyTorch实现。
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[[autodoc]] StoppingCriteria
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- __call__
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[[autodoc]] StoppingCriteriaList
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- __call__
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[[autodoc]] MaxLengthCriteria
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- __call__
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[[autodoc]] MaxTimeCriteria
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- __call__
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## Constraints
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可以使用[`Constraint`]来强制生成结果包含输出中的特定tokens或序列。请注意，这仅适用于我们的PyTorch实现。
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[[autodoc]] Constraint
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[[autodoc]] PhrasalConstraint
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[[autodoc]] DisjunctiveConstraint
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[[autodoc]] ConstraintListState
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## BeamSearch
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[[autodoc]] BeamScorer
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- process
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- finalize
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[[autodoc]] BeamSearchScorer
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- process
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- finalize
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[[autodoc]] ConstrainedBeamSearchScorer
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- process
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- finalize
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## Utilities
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[[autodoc]] top_k_top_p_filtering
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[[autodoc]] tf_top_k_top_p_filtering
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## Streamers
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[[autodoc]] TextStreamer
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[[autodoc]] TextIteratorStreamer
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