pipe=pipeline(task="text-generation",model=model,tokenizer=tokenizer,return_full_text=True,**generation_config)pipe(query,add_special_tokens=True)
from transformers import pipeline, set_seedset_seed(42)pipe = pipeline("text-generation", model="gpt2-xl")gpt2_query = sample_text + "\nTL;DR:\n"pipe_out = pipe(gpt2_query, max_length=512, clean_up_tokenization_spaces=True)summaries["gpt2"] = "\n".join(sent_tokenize(pipe_out[0...
generator = pipeline("text-generation") response ="Dear Bumblebee, I am sorry to hear that your order was mixed up."prompt = text +"\n\nCustomer service response:\n"+ response outputs = generator(prompt, max_length=200)print(outputs[0]['generated_text']) Dear Amazon, last week I ord...
out = pipe(prompt, num_return_sequences=num_return_sequences, clean_up_tokenization_spaces=True)return"\n".join(f"{i+1}."+ s["generated_text"]fori, sinenumerate(out)) prompt ="\nWhen they came back"print("GPT completions:\n"+ enum_pipeline_ouputs(generation_gpt, prompt,3))print(...
from transformers import pipeline # Create a text generation pipeline text_generator = pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B") # Provide a prompt for marketing copy prompt = "Create marketing copy for a new smartphone that emphasizes its camera features." ...
text_generator=pipeline("text-generation",model="EleutherAI/gpt-neo-1.3B")# Provide a promptformarketing copy prompt="Create marketing copy for a new smartphone that emphasizes its camera features."marketing_copy=text_generator(prompt,num_return_sequences=1)# Print the generated marketing copyprint...
🤗 Transformer库中最基本的对象是pipeline(管道),将模型与其他必要预处理和后处理步骤组合起来,使我们可以直接输入任何文本并获得可理解的答案: from transformers import pipeline classifier = pipeline("sentiment-analysis") classifier( [ "This restaurant is awesome", ...
Text generation generator=Informers.pipeline("text-generation")generator.("I enjoy walking with my cute dog,") Text-to-text generation text2text=Informers.pipeline("text2text-generation")text2text.("translate from English to French: I'm very happy") ...
nlpbloomdistributed-systemsmachine-learningdeep-learningchatbotpytorchfalcontransformerneural-networksllamagptpretrained-modelslanguage-modelsvolunteer-computingpipeline-parallelismguanacotensor-parallelismlarge-language-modelsmixtral UpdatedSep 7, 2024 Python Load more… ...
>>>fromtransformersimportpipeline# 使用情绪分析流水线>>>classifier = pipeline('sentiment-analysis')>>>classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label':'POSITIVE','score':0.9996980428695679}]