Promptzl

pr？ mptzl

將最新的LLMS變成零⁺ -Shot Pytorch分類器，僅幾行代碼。

提示提供：

• ？零⁺ - 用LLMS彈出分類
• ？將因果關係和蒙版LMS變成分類器，而無需任何培訓
• ？設備上的批處理處理以提高效率
• 加速致電在線API
• ？通過本地使用該模型的透明度和可訪問性
• ？標籤上的分佈
• ✂️無需從答案中提取預測。

有關更多信息，請查看官方文檔。

安裝

pip install -U promptzl

入門

在僅幾行代碼中，您可以將選擇的LLM轉換為具有所有理想屬性的老式分類器：

設置數據集：

 from datasets import Dataset

dataset = Dataset . from_dict (
    {
        'text' : [
            "The food was absolutely wonderful, from preparation to presentation, very pleasing." ,
            "The service was a bit slow, but the food made up for it. Highly recommend the pasta!" ,
            "The restaurant was too noisy and the food was mediocre at best. Not worth the price." ,
        ],
        'label' : [ 1 , 1 , 0 ]
    }
)

定義一個提示，將語言模型引導到正確的預測：

 from promptzl import FnVbzPair , Vbz
prompt = FnVbzPair (
    lambda e : f"""Restaurant review classification into categories 'positive' or 'negative'.

    'Best pretzls in town!'='positive'
    'Rude staff, horrible food.'='negative'

    ' { e [ 'text' ] } '=""" ,
    Vbz ({ 0 : [ "negative" ], 1 : [ "positive" ]}))

初始化模型：

 from promptzl import CausalLM4Classification
model = CausalLM4Classification (
    'HuggingFaceTB/SmolLM2-1.7B' ,
    prompt = prompt )

對數據進行分類：

 from sklearn . metrics import accuracy_score
output = model . classify ( dataset , show_progress_bar = True , batch_size = 1 )
accuracy_score ( dataset [ 'label' ], output . predictions )
1.0

有關更多詳細的教程，請查看文檔！