这种采样引入的随机性有助于在很多情况下生成的质量。 Adding some randomness helps make output text more natural. In top-3 decoding, we first shortlist three tokens then sample one of them considering their likelihood scores. 更广泛地说,选择前三个tokens意味着将 top-k 参数设置为 3。更改 top-k ...
generate( inputs, attention_mask=attention_mask, max_length=50, do_sample=True, top_k=top_k, # 设置 Top-K top_p=top_p, # 设置 Top-P temperature=temperature, # 控制生成的随机性 no_repeat_ngram_size=2, # 防止重复 n-gram pad_token_id=tokenizer.eos_token_id ) # 解码生成的文本 ...
这种采样引入的随机性有助于在很多情况下生成的质量。 Adding some randomness helps make output text more natural. In top-3 decoding, we first shortlist three tokens then sample one of them considering their likelihood scores. 更广泛地说,选择前三个tokens意味着将 top-k 参数设置为 3。更改 top-k ...
服装行业的FIT样,PP样,TOP样等1、InitialSample——首件样品:根据设计图做的首件,主要是确认款式、看效果。2、FitSample——试穿样:一般跟SizeSetSample一起,主要看真人模特穿起的效果以及尺寸问题。3、SALESMANSample——销售样:客人用于展销会的展示样,目的为了将之前所做出的新款式,通过展销办获得定单。一般销售...
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随机贪婪搜索:当num_beams=1且do_sample=True时,每个单步时会根据模型输出的概率进行采用,而不是选条件概率最高的词,增加多样性。调用 sample() 方法 贪婪柱搜索:当num_beams>1且do_sample=False时,做一个num_beams的柱搜索,每次都是贪婪选择top N个柱。调用 beam_search() 方法 ...
目前支持这些参数:do_sample\temperature\max_length\num_beams\top_p\logits_processor 举个例子,如果不用sample,想用greedy search:inputs = {'text':'请写一首五言绝句。', 'history': [], 'do_sample':False}print(pipe(inputs)) 这样生成的内容就每次一样了。 建议模型提供方、平台方完善模型文档说明...
本文以 huggingface-transformers 的文本生成解码代码为例,对文本生成常用的五种解码策略 greedy search、beam search、sample、sample and rank & beam sample、group beam search 进行逐行解读。每一小节首先会介绍对应解码策略的原理,接着给出供大家快速上手的代码示例,并逐层介绍调用过程,最后给出所使用到的所有类...
But before I could reach that, I encountered a lot of problems with my top-p sampling. Here's the code snippet: generation_args = { "max_new_tokens": 500, "temperature": 0.4, # Adjust temperature if needed for more or less randomness "do_sample": True, # Enable samplin...