MTEB: Massive Text Embedding Benchmark
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Updated
Jul 16, 2024 - Python
MTEB: Massive Text Embedding Benchmark
Generative Representational Instruction Tuning
Forecasting the Adoption Process of Technology Using AI Methods
Generative AI & Recommendation Engine --- Firat University / Faculty of Technology / Software Engineering / Final Project
Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
Building a Medical chatbot using SBERT model. Dataset used was MEDQUAD
Embedding Representation for Indonesian Sentences!
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
An implementation of the TaxRetrievalBenchmark task for the 🤗 Massive Text Embedding Benchmark (MTEB) framework.
This project is a corporate partnership with the online bookstore platform 'YES24', where we collect data from various platforms such as YouTube to analyze the latest trends and develop a service that recommends books matching these trends.
To enhance the interaction between local governments and the communities they represent, we leveraged the power of AI to simplify the complaint management process for government entities.
Interactive tree-maps with SBERT & Hierarchical Clustering (HAC)
TextReducer - A Tool for Summarization and Information Extraction
文本相似度,语义向量,文本向量,text-similarity,similarity, sentence-similarity,BERT,SimCSE,BERT-Whitening,Sentence-BERT, PromCSE, SBERT
MTP-FlanT5-SBERT-Model-for-NewsQA-and-Teacher-Student-Model
emoji_finder
Developed a versatile framework for analyzing and classifying any text based on linguistic and thematic elements
This repository explores enhancing dialogue summarization with commonsense knowledge through the SICK framework, evaluating models on dialogue datasets to assess commonsense's impact on summarization quality.
This project builds a semantic search engine specifically designed for video content. It utilizes SBERT, to understand the meaning behind user queries and videos. This allows users to search for specific information within videos, skipping irrelevant parts and saving them valuable time.
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