开始使用Gemini

Your task is to summarize an abstract into one sentence. 

Avoid technical jargon and explain it in the simplest of words.

Abstract: Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance.

Antibiotics are medicines used to kill or stop the growth of bacteria causing infections, but they don't work against viruses.

Your task is to extract model names from machine learning paper abstracts. Your response is an array of the model names in the format [\"model_name\"]. If you don't find model names in the abstract or you are not sure, return [\"NA\"]

Abstract: Large Language Models (LLMs), such as ChatGPT and GPT-4, have revolutionized natural language processing research and demonstrated potential in Artificial General Intelligence (AGI). However, the expensive training and deployment of LLMs present challenges to transparent and open academic research. To address these issues, this project open-sources the Chinese LLaMA and Alpaca…

[\"LLMs\", \"ChatGPT\", \"GPT-4\", \"Chinese LLaMA\", \"Alpaca\"]

Your task is to classify a piece of text, delimited by triple backticks, into the following emotion labels: ["anger", "fear", "joy", "love", "sadness", "surprise"]. Just output the label as a lowercase string.
Text: I feel very angry today
Emotion: anger
Text: Feeling thrilled by the good news today.
Emotion: joy
Text: I am actually feeling good today.
Emotion:

"""
At the command line, only need to run once to install the package via pip:
 
$ pip install google-generativeai
"""
 
import google.generativeai as genai
 
genai.configure(api_key="YOUR_API_KEY")
 
# Set up the model
generation_config = {
  "temperature": 0,
  "top_p": 1,
  "top_k": 1,
  "max_output_tokens": 2048,
}
 
safety_settings = [
  {
    "category": "HARM_CATEGORY_HARASSMENT",
    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  },
  {
    "category": "HARM_CATEGORY_HATE_SPEECH",
    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  },
  {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  },
  {
    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  }
]
 
model = genai.GenerativeModel(model_name="gemini-pro",
                              generation_config=generation_config,
                              safety_settings=safety_settings)
 
prompt_parts = [
  "Your task is to extract model names from machine learning paper abstracts. Your response is an array of the model names in the format [\\\"model_name\\\"]. If you don't find model names in the abstract or you are not sure, return [\\\"NA\\\"]\n\nAbstract: Large Language Models (LLMs), such as ChatGPT and GPT-4, have revolutionized natural language processing research and demonstrated potential in Artificial General Intelligence (AGI). However, the expensive training and deployment of LLMs present challenges to transparent and open academic research. To address these issues, this project open-sources the Chinese LLaMA and Alpaca…",
]
 
response = model.generate_content(prompt_parts)
print(response.text)

[\"LLMs\", \"ChatGPT\", \"GPT-4\", \"Chinese LLaMA\", \"Alpaca\"]