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Notellm
Overview :
NoteLLM is a retrieval-based large language model focused on user-generated content, aiming to enhance the performance of recommendation systems. By combining topic generation with embedding generation, NoteLLM improves its ability to understand and process note content. The model adopts an end-to-end fine-tuning strategy, supporting multi-modal inputs, which enhances its application potential in diversified content domains. Its importance lies in effectively improving the accuracy of note recommendations and user experience, especially suitable for UGC platforms like Xiaohongshu.
Target Users :
This product is suitable for data scientists, machine learning researchers, and developers who want to improve recommendation systems, especially in handling user-generated content (UGC). Its unique multi-modal processing capabilities and efficient embedding generation mechanism give it an advantage in practical applications.
Use Cases
Performing note content recommendations on Xiaohongshu platform to enhance user experience.
Providing personalized learning note generation and recommendation for educational applications.
In social media analysis, quickly generating topic tags to enhance content exposure.
Features
Generate topic tags and categories to improve content embedding quality.
Support multi-modal input processing to adapt to complex content types.
Provide an end-to-end fine-tuning strategy without alignment to increase efficiency.
Include effective mechanisms (mICL and late fusion) to enhance multi-modal representation.
Offer a complete framework for training and evaluation, facilitating experiments and applications.
Easy to integrate and use, suitable for rapid development and deployment.
Model design based on deep learning, supporting large-scale data processing.
Open-source code available, facilitating community contributions and modifications.
How to Use
Access the GitHub page of NoteLLM and clone the repository.
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Run the env.sh script to set up the required environment.
Download and prepare pre-trained weights, placing them in the designated directory.
Configure the dataset as needed, ensuring the data format meets requirements.
Run the main training script for model training or evaluation.
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