H4RB1D3L

Scikit-LLM: Sklearn Meets Large Language Models! Seamlessly integrate powerful language models like ChatGPT into the scikit-learn ecosystem for enhanced text analysis tasks. It aims to provide a convenient interface for leveraging LLMs in various machine learning pipelines, particularly those involving text data. Here's what you can do with Scikit-LLM: 🔹Zero-Shot Text Classification: It offers a `ZeroShotGPTClassifier` that allows text classification without re-training the model. The classifier works with descriptive labels. 🔸Few-Shot Text Classification The `FewShotGPTClassifier` allows for few-shot classification, where training samples are added to the prompt and passed to the model. 🔹Dynamic Few-Shot Text Classification: The `DynamicFewShotGPTClassifier` dynamically selects samples per class to include in the prompt, allowing the classifier to scale to larger datasets. 🔸Text Classification with Google PaLM 2: It supports PaLM-based models like `ZeroShotPaLMClassifier`, `PaLMClassifier`, and `PaLM` for various text classification tasks. 🔹Text Vectorization: The `GPTVectorizer` can be used for data preprocessing to embed text into fixed-dimensional vectors. 🔸LLM Fine-Tuning: It supports fine-tuning scenarios for text classification and text-to-text tasks. 🔹Text Summarization: The `GPTSummarizer` can be used as a stand-alone estimator or as a preprocessor for text summarization. 🔸Text Translation: The `GPTTranslator` allows translating text into different languages. I have linked the official GitHub repo in next tweet! If you liked this content & are interested in: - Python 🐍 - ML/MLOps 🛠 - CV/NLP 🗣 - LLMs 🧠 Find me → @akshay_pachaar ✔️ Everyday, I share tutorials on above topics! Thanks for reading!🥂