Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting a wide range of text-embedding models and frameworks.
-
Updated
Jun 19, 2024 - Python
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting a wide range of text-embedding models and frameworks.
Semantic embedding-based system for question answering from PDFs with visual analysis tools.
Automated discovery and classification of websites content through unsupervised learning approach
This repo contains everything about transformers and NLP.
training literature bert classification.
Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.
Review: Deep Learning for Sentence Semantic Similarity
A Retrieval-Augmented Generation (RAG) System for PDF Chat using Qdrant Vector Database.
RAG (Retrieval Augmented Generation) and vector search to translate natural language into SQL queries for PostgreSQL databases.
RAG architecture to retrieve and embed pdfs
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
Implementation of the work "ShareBERT: Embeddings Are Capable of Learning Hidden Layers".
Tranformer-based Denoising AutoEncoder for Sentence Transformers Unsupervised pre-training.
This project implements a semi-supervised approach to classify UN speeches. Utilized BERT, Gensim, Node2Vec and Tensorflow
Portfolio - BERT Recommender System
VITS2 extended with XPhoneBERT encoder
CBERTdp is a strategy to speed up the clssification task by clustering BERT embeddings using different methods in order to use K-Means and the Dot-Product to obtaint the prediction results
A text classification using Google BERT and Tensorflow to classify if an email is spam or not spam
Add a description, image, and links to the bert-embeddings topic page so that developers can more easily learn about it.
To associate your repository with the bert-embeddings topic, visit your repo's landing page and select "manage topics."