Search Results
Fourier Contour Embedding for Arbitrary-Shaped Text Detection
One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances.
Multilingual E5 Text Embeddings: A Technical Report
This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023.
Text Embeddings by Weakly-Supervised Contrastive Pre-training
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.
Ranked #11 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
MTEB Benchmark
Only Connect Walls Dataset Task 1 (Grouping)
+1
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval, which provides a unified model foundation for real-world IR applications.
VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Thirdly, we introduce a multi-stage training algorithm, which first aligns the visual token embedding with the text encoder using massive weakly labeled data, and then develops multi-modal representation capability using the generated composed image-text data.
Ranked #9 on
Image Retrieval
on CIRR
MTEB: Massive Text Embedding Benchmark
MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages.
Ranked #1 on
Text Retrieval
on MTEB
The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding
The evaluation of English text embeddings has transitioned from evaluating a handful of datasets to broad coverage across many tasks through benchmarks such as MTEB.
MMTEB: Massive Multilingual Text Embedding Benchmark
MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date.
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
Enhanced Network Embedding with Text Information
TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information.
