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from transformers import AutoModelForCausalLM
from transformers import GPT2Tokenizer
from transformers.models.gpt2.modeling_gpt2 import GPT2Model, GPT2LMHeadModel
if __name__ == "__main__":
gpt2_tokenizer: GPT2Tokenizer = GPT2Tokenizer.from_pretrained("/Users/wangjianing/Desktop/开源代码与数据模型/模型/gpt2")
# gpt2_model = GPT2LMHeadModel.from_pretrained("/Users/wangjianing/Desktop/开源代码与数据模型/模型/gpt2")
# # input_text = "The capital city of China is Beijing. The capital city of Japan is Tokyo. The capital city of America"
# input_text = "What are follows emotions? \n\n The book is very nice.\n great. \n\n I never eat chocolate!\n bad. \n\n This film is wonderful.\n Great"
# # input_text = "Mr. Chen was born in Shanghai. Obama was born in US. Trump was born in"
# inputs = gpt2_tokenizer(input_text, return_tensors="pt")
# print(inputs)
# output = gpt2_model(**inputs)
# # print(output["last_hidden_state"])
# # print(output["last_hidden_state"].size())
# print(output["logits"])
# print(output["logits"].size())
# gen_output = gpt2_model.generate(**inputs, max_length=60)
# # gen_result = gpt2_tokenizer.convert_ids_to_tokens(gen_output[0])
# gen_result = gpt2_tokenizer.decode(gen_output[0])
# print(gen_result)
gpt2_tokenizer(
[["What are follows emotions?", "What are follows emotions?"], ["What are follows emotions?"]],
truncation=True,
max_length=30,
padding="max_length",
return_offsets_mapping=True
)
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