Easy text classification for everyone : Bert based models via Huggingface transformers (KR / EN)
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Updated
Nov 23, 2020 - Python
Easy text classification for everyone : Bert based models via Huggingface transformers (KR / EN)
huggingface-go : 加速下载 huggingface 的模型和数据集
Upload & Merge CSV or JSON Data with Images to Notion Database
Simple Generative AI enabled Streamlit web application that converts speech to-image.
This repository contains code for generating blog content using the LLama 2 language model. It integrates with Streamlit for easy user interaction. Simply input your blog topic, desired word count, and writing style to generate engaging blog content.
An AI powered CLI tool that can help you organize and make projects the fastest way possible.
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multiple anime image classification / anime image checker
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Data Dialogue enables natural language querying of databases by integrating LLMs with SQL databases.
🤗 A Python script for efficiently downloading and reconstructing large Hugging Face model files by splitting them into manageable chunks
Chroma DB vector database, with embedding and reranker models to implement a Retrieval Augmented Generation (RAG) system.
🤖 Dermify.AI is an ⚡ AI powered Web Application which harnesses the power of image processing to offer cost-effective and accessible skin condition assessments worldwide
LangLens is an LLM model based on Openai gpt-3.5 and Salesforce vqa base model. For now, it can caption, detect objects in the image (perfectly) and answer some basic questions related to the image (to be fine tuned).
My first open source LLM API deployment - BritneyBot!
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An LLM app leveraging RAG with LangChain and GPT-4 mini to analyze earnings call transcripts, assess company performance, using natural language queries (NLP), FAISS (vector database), and Hugging Face re-ranking models.
UniversalLLMAdapter class initializes the appropriate client based on the specified provider.
Welcome to the Medical Chatbot Assistant project! This repository contains a powerful and efficient medical chatbot built using the LLaMA 2 model, Hugging Face embeddings, and Pinecone vector database. The chatbot is designed to assist users with medical inquiries, providing reliable and accurate responses.
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