top_p和top_k都是用来控制文本生成过程中随机抽样策略的参数,它们的主要区别在于筛选候选词的方式和目的不同。从默认值来看,k取值是一个比较大的整数,而p是一个浮点数,默认1.0,啥意思呢? 以下是两者各自的定义和区别: Top-K Sampling: 定义:在生成下一个单词时,top_k参数指定了从概率分布中保留概率最高的k...
# set seed to reproduce results. Feel free to change the seed though to get different resultstf.random.set_seed(0)# activate sampling and deactivate top_k by setting top_k sampling to 0sample_output=model.generate(input_ids,do_sample=True,max_length=50,top_k=0)print("Output:\n"+100*...
在《GPT2-Large模型解码方法比较》中显示了Beam search方法比greedy search方法的效果好,本文接着比较另外两种解码方法: Top-K sampling和Top-p sampling。 2 Top-K sampling Facebook的Fan等人(2018)在他们的论文《Hierarchical Neural Story Generation(分层神经故事的产生)》引入了一个简单但非常强大的取样方案,称...
5、Top-P (Nucleus) Sampling: Nucleus Sampling(核采样),也被称为Top-p Sampling旨在在保持生成文本质量的同时增加多样性。这种方法可以视作是Top-K Sampling的一种变体,它在每个时间步根据模型输出的概率分布选择概率累积超过给定阈值p的词语集合,然后在这个词语集合中进行随机采样。这种方法会动态调整候选词语的数量...
This document discusses different decoding strategies for generating text with language models, focusing on top-k sampling and top-p sampling to pick output tokens based on likelihood scores.
We noticed that there are a little differences in the implementation of top_p and top_k in the vLLM sampler compared to Huggingface's implementation. We have aligned the implementation details of T...
As mentioned in #81 (comment), the current PyTorch-based top-k and top-p implementation is memory-inefficient. This can be improved by introducing custom kernels.
While most active learning methods for this problem follow the incremental query learning paradigm in which the classifier is retained upon each newly labelled query, we present a distance-based method which samples the top-k representative data simultaneously and can be applied to any distance-based...
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Top-K 采样: 限制候选词汇数量。 Top-P 采样(Nucleus Sampling): 根据累积概率选择候选词汇,动态调整词汇集。 为了直观叙述,假设我们当前的概率分布为: 词汇概率 A 0.4 B 0.3 C 0.2 D 0.05 <eos> 0.05 Top-K 采样详解 工作原理 Top-K 采样是一种通过限制候选词汇数量来增加生成文本多样性的方法。在每一...