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咱们以前介绍过 Pandas 和 ChaGPT 整合，这样能够不理解 Pandas 的状况下对 DataFrame 进行操作。当初又有人开源了 Scikit-LLM，它联合了弱小的语言模型，如 ChatGPT 和 scikit-learn。但这个并不是让咱们自动化 scikit-learn，而是将 scikit-learn 和语言模型进行整合，scikit-learn 也能够解决文本数据了。
装置
pip install scikit-llm既然要与 Open AI 的模型整合，就须要他的 Key，从 Scikit-LLM 库中导入 SKLLMConfig 模块，并增加 openAI 密钥:
# importing SKLLMConfig to configure OpenAI API (key and Name)
fromskllm.configimportSKLLMConfig
# Set your OpenAI API key
SKLLMConfig.set_openai_key("<YOUR_KEY>")
# Set your OpenAI organization (optional)
SKLLMConfig.set_openai_org("<YOUR_ORGANIZATION>")ZeroShotGPTClassifier
通过整合 ChatGPT 不须要专门的训练就能够对文本进行分类。ZeroShotGPTClassifier，就像任何其余 scikit-learn 分类器一样，应用非常简单。
# importing zeroshotgptclassifier module and classification dataset
fromskllmimportZeroShotGPTClassifier
fromskllm.datasetsimportget_classification_dataset
# get classification dataset from sklearn
X, y=get_classification_dataset()
# defining the model
clf=ZeroShotGPTClassifier(openai_model="gpt-3.5-turbo")
# fitting the data
clf.fit(X, y)
# predicting the data
labels=clf.predict(X)Scikit-LLM 在后果上通过了非凡解决，确保响应只蕴含一个无效的标签。如果响应短少标签，它还能够进行填充，依据它在训练数据中呈现的频率为你抉择一个标签。
对于咱们本人的带标签的数据，只须要提供候选标签的列表，代码是这个样子的：
# importing zeroshotgptclassifier module and classification dataset
fromskllmimportZeroShotGPTClassifier
fromskllm.datasetsimportget_classification_dataset
# get classification dataset from sklearn for prediction only
X, _=get_classification_dataset()
# defining the model
clf=ZeroShotGPTClassifier()
# Since no training so passing the labels only for prediction
clf.fit(None, ['positive', 'negative', 'neutral'])
# predicting the labels
labels=clf.predict(X)MultiLabelZeroShotGPTClassifier
多标签也相似
# importing Multi-Label zeroshot module and classification dataset
fromskllmimportMultiLabelZeroShotGPTClassifier
fromskllm.datasetsimportget_multilabel_classification_dataset
# get classification dataset from sklearn
X, y=get_multilabel_classification_dataset()
# defining the model
clf=MultiLabelZeroShotGPTClassifier(max_labels=3)
# fitting the model
clf.fit(X, y)
# making predictions
labels=clf.predict(X)创立 MultiLabelZeroShotGPTClassifier 类的实例时，指定要调配给每个样本的最大标签数量（这里:max_labels=3）
数据没有没有标签怎么办？能够通过提供候选标签列表来训练没有标记数据的分类器。y 的类型应该是 List[List[str]]。上面是一个没有标记数据的训练示例:
# getting classification dataset for prediction only
X, _=get_multilabel_classification_dataset()
# Defining all the labels that needs to predicted
candidate_labels= [
"Quality",
"Price",
"Delivery",
"Service",
"Product Variety"
]
# creating the model
clf=MultiLabelZeroShotGPTClassifier(max_labels=3)
# fitting the labels only
clf.fit(None, [candidate_labels])
# predicting the data
labels=clf.predict(X)文本向量化
文本向量化是将文本转换为数字的过程，Scikit-LLM 中的 GPTVectorizer 模块，能够将一段文本 (无论文本有多长) 转换为固定大小的一组向量。
# Importing the necessary modules and classes
fromsklearn.pipelineimportPipeline
fromsklearn.preprocessingimportLabelEncoder
fromxgboostimportXGBClassifier
# Creating an instance of LabelEncoder class
le=LabelEncoder()
# Encoding the training labels 'y_train' using LabelEncoder
y_train_encoded=le.fit_transform(y_train)
# Encoding the test labels 'y_test' using LabelEncoder
y_test_encoded=le.transform(y_test)
# Defining the steps of the pipeline as a list of tuples
steps= [('GPT', GPTVectorizer()), ('Clf', XGBClassifier())]
# Creating a pipeline with the defined steps
clf=Pipeline(steps)
# Fitting the pipeline on the training data 'X_train' and the encoded training labels 'y_train_encoded'
clf.fit(X_train, y_train_encoded)
# Predicting the labels for the test data 'X_test' using the trained pipeline
yh=clf.predict(X_test)文本摘要
GPT 十分善于总结文本。在 Scikit-LLM 中有一个叫 GPTSummarizer 的模块。
# Importing the GPTSummarizer class from the skllm.preprocessing module
from skllm.preprocessing import GPTSummarizer
# Importing the get_summarization_dataset function
from skllm.datasets import get_summarization_dataset
# Calling the get_summarization_dataset function
X = get_summarization_dataset()
# Creating an instance of the GPTSummarizer
s = GPTSummarizer(openai_model='gpt-3.5-turbo', max_words=15)
# Applying the fit_transform method of the GPTSummarizer instance to the input data 'X'.
# It fits the model to the data and generates the summaries, which are assigned to the variable 'summaries'
summaries = s.fit_transform(X)须要留神的是，max_words 超参数是对生成摘要中单词数量的灵便限度。尽管 max_words 为摘要长度设置了一个粗略的指标，但摘要器可能偶然会依据输出文本的上下文和内容生成略长的摘要。
总结
ChaGPT 的火爆使得泛化模型有了更多的提高，这种提高也给咱们日常的应用带来了微小的改革，Scikit-LLM 就将 LLM 整合进了 Scikit 的工作流，如果你有趣味，这里是源码：
https://avoid.overfit.cn/post/9ba131a01d374926b6b7efff97f61c45
作者：Fareed Khan