文本解码就是根据当前已经输入的内容不断地预测下一个词，前期通过大量的文本文章等输入，让模型学习好以后，根据已学习的内容，不断预测下一个词。就像鹦鹉学舌一样你不断的叫他说你好大帅哥，你好大帅哥。后面某一天，当你说你好的时候，他会自然的接着说大帅哥。文本解码同理。
不过内容量会大很多，除了会说你好大帅哥，也会说你好大美女。那AI是怎么知道应该说哪个。他会看前文，因为我们喂给他文章里面，“女”这个词总是关联出现大美女，所以当前面出现女，接着说你好的时候，他就知道大美女的概率高于大帅哥，就是优先出现大帅哥。
import mindspore
from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')
# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')
# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')
mindspore.set_seed(0)
# set top_k = 50 and set top_p = 0.95 and num_return_sequences = 3
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=50,
top_k=5,
top_p=0.95,
num_return_sequences=3
)
print("Output:\n" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))
Output:
----------------------------------------------------------------------------------------------------
0: I enjoy walking with my cute dog.
"My dog loves the smell of the dog. I'm so happy that she's happy with me.
"I love to walk with my dog. I'm so happy that she's happy
1: I enjoy walking with my cute dog. I'm a big fan of my cat and her dog, but I don't have the same enthusiasm for her. It's hard not to like her because it is my dog.
My husband, who
2: I enjoy walking with my cute dog, but I'm also not sure I would want my dog to walk alone with me."
She also told The Daily Beast that the dog is very protective.
"I think she's very protective of
类似 这个示例，当输入I enjoy walking with my cute dog的时候，AI会一直续写下去，总体看上去，效果还是很不错的。
