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Implementation of the models of the Universal-NER Paper 2024 using a Streamlit-based web application that is designed to process PDF documents for Named Entity Recognition tasks. It allows users to upload PDF files, from which the application extracts text, images, and tables to identify entities based on a user-specific user-specified entity type.
M3DBench introduces a comprehensive 3D instruction-following dataset with support for interleaved multi-modal prompts. Furthermore, M3DBench provides a new benchmark to assess large models across 3D vision-centric tasks.
This repository contains the implementation of a fine-tuned Llama2 chatbot using QLoRA, tailored to provide detailed information and recommendations about movies. The model is fine-tuned on the IMDB dataset, enabling it to generate informed and contextually relevant responses.