MTEB: Massive Text Embedding Benchmark
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Updated
Aug 16, 2024 - Python
MTEB: Massive Text Embedding Benchmark
Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard
Topic Embedding, Text Generation and Modeling using diffusion
[ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings
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PL-MTEB: Polish Massive Text Embedding Benchmark
Codebase for RetroMAE and beyond.
MS-marco-MiniLM-L-12-v2 model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order.
This is a sentence embedding model, initialized from xlm-roberta-large and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation.
BGE-M3 is an innovative project known for its versatility, featuring Multi-Functionality, Multi-Linguality, and Multi-Granularity.
jina-embeddings-v2-base-en is an English, monolingual embedding model supporting 8192 sequence length. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone jina-bert-v2-base-en is pretrained on the C4 dataset.
Image Steganography GUI | Easily Hide Text Files within Images with User-Friendly GUI | Pyton Tool
This git repository hosts a user interface for a chat-app, with integrated text similarity search for querying a document. Think of it as an upgrded Cmd+F search. It's written in Pure Python. Created for Learning Purposes.
A website that visualises how text embeddings trained on Google News are biased towards gender. It also functions as an open source database to generate text embeddings .
A project aiming to leverage text embeddings and Milvus, a high-performance vector search engine, to detect duplicate job postings.
MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval). MedCPT Query Encoder: compute the embeddings of short texts (e.g., questions, search queries, sentences). In this template, we will import the MedCPT Query Encoder on the Inferless Platform.
OpenAI Text Embedding. Clean, process and create vectorize representation of text for indexing and semantic search
Flask API for generating text embeddings using OpenAI or sentence_transformers
Universal-Sentence-Encoder-Multilingual-QA is a model developed by researchers at Google mainly for the purpose of question answering. You can use this template to import the model in Inferless.
Retrieve text embeddings, but cache them locally if we have already computed them.
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