Introducing Scikit-LLM: Sklearn Meets Large Language Models
Scikit-LLM is a powerful Python library that combines the popular machine learning framework Scikit-learn with large language models, such as GPT-3 and BERT. This library aims to make it easier for developers to leverage the power of large language models in their machine learning projects.
Why Scikit-LLM?
Large language models have revolutionized natural language processing tasks, such as text generation, sentiment analysis, and machine translation. However, integrating these models into traditional machine learning pipelines can be challenging. Scikit-LLM aims to bridge this gap by providing a user-friendly interface for working with large language models within the Scikit-learn ecosystem.
Features
Some key features of Scikit-LLM include:
- Integration with popular large language models, such as GPT-3 and BERT
- Easy-to-use API for training and deploying models
- Support for a wide range of natural language processing tasks
- Compatibility with Scikit-learn’s data preprocessing and evaluation tools
Getting Started
To get started with Scikit-LLM, simply install the library using pip:
pip install scikit-llm
Once the library is installed, you can start using it in your Python scripts by importing the necessary modules:
import scikit_llm
Example Usage
Here is an example of how you can use Scikit-LLM to perform sentiment analysis on a dataset of movie reviews:
from scikit_llm import SentimentAnalyzer
data = load_movie_reviews()
X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'])
analyzer = SentimentAnalyzer(model='gpt-3')
analyzer.fit(X_train, y_train)
predictions = analyzer.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy}')
Conclusion
Scikit-LLM offers a seamless way to incorporate large language models into your machine learning projects. By combining the power of Scikit-learn with the capabilities of state-of-the-art language models, developers can unlock new possibilities in natural language processing. Give Scikit-LLM a try in your next project and see the difference it can make!
If I have to use Azure Open AI how can I do it .Even though I am using the model
ZeroShotGPTClassifier(openai_model="azure::gpt-3.5-turbo")
I am getting below error
Could not obtain the completion after 3 retries: `InvalidRequestError :: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again.`
None
Could not extract the label from the completion: 'NoneType' object is not subscriptable
Suggestion – The text aren't clear inside the boxes. Keep the boxes transparent. Avoid color. That will probably make it more readable.