jina-embeddings-v3: Multilingual Embeddings With Task LoRA research paper by Jina AI, 2024
Jina AI · Training and scaling · Sep 16, 2024 · 221 citations · 35 upvotes · unverified
What it shows
jina-embeddings-v3, a large-scale text embedding model, achieves state-of-the-art performance in multilingual and long-context retrieval tasks using Low-Rank Adaptation and Matryoshka Representation Learning.
By Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram and 9 more · arXiv 2409.10173 · PDF
UnverifiedHugging Face's summary; not yet checked by hand.
More from Jina AI
All 7jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower CompositionFrozen-encoder model composition enables efficient multimodal embedding by freezing pre-trained text models and adding specialized encoders for images and audio while maintaining text embedding consistency.
Jina AI11 citations10 upvotesRead →jina-embeddings-v5-text: Task-Targeted Embedding DistillationCompact text embedding models are developed through a combined training approach using distillation and contrastive loss, achieving state-of-the-art performance while supporting long-context sequences and efficient quantization.
Jina AI28 citations28 upvotesRead →Jina-VLM: Small Multilingual Vision Language ModelJina-VLM, a 2.4B parameter vision-language model, achieves top performance in multilingual visual question answering using a SigLIP2 vision encoder and Qwen3 language backbone with an attention-pooling connector.
Jina AI3 citations17 upvotesRead →jina-reranker-v3: Last but Not Late Interaction for Document RerankingA multilingual document reranker using causal self-attention achieves state-of-the-art performance with a compact architecture.
Jina AI45 citations13 upvotesRead →Efficient Code Embeddings from Code Generation ModelsJina-code-embeddings uses an autoregressive backbone pre-trained on text and code to generate embeddings for code retrieval, question-answering, and similarity identification.
Jina AI23 citations20 upvotesRead →Jina CLIP: Your CLIP Model Is Also Your Text RetrieverA novel multi-task contrastive training method improves CLIP model performance on both text-image and text-text retrieval tasks.
Jina AI61 citations37 upvotesRead →
Other training and scaling papers
TopicLoRA: Low-Rank Adaptation of Large Language ModelsLoRA fine-tunes a large model by training small low-rank matrices, cutting trainable parameters by 10,000 times.
Microsoft25.6k citations66 upvotesRead →Scaling Laws for Neural Language ModelsLanguage model loss falls as a smooth power law as model size, data and compute grow.
OpenAI9,592 citations12 upvotesRead →Training Compute-Optimal Large Language ModelsChinchilla: for a fixed compute budget, train a smaller model on more data; parameters and tokens should grow together.
Google DeepMind4,035 citations13 upvotesRead →Megatron-LM: Training Multi-Billion Parameter Language Models Using Model ParallelismMegatron-LM splits each Transformer layer across GPUs to train models with billions of parameters.
NVIDIA3,387 citations5 upvotesRead →DeepSeek LLM: Scaling Open-Source Language Models with LongtermismDeepSeek LLM, an open-source language model project, develops a large dataset and employs SFT and DPO to achieve performance surpassing LLaMA-2 70B and GPT-3.5 in various benchmarks and open-ended evaluations.
DeepSeek872 citations56 upvotesRead →LoRA Learns Less and Forgets LessLoRA learns less than full fine-tuning on code and math, but forgets less of what the model already knew.
Databricks486 citations91 upvotesRead →
About this paper
- Venue
- arXiv.org
- Citations
- 221, 9 influential · Semantic Scholar
- Upvotes
- 35 · Hugging Face
- Lab
- Jina AI · on Companies · on Acquisitions · on Downtime · on labs · on marketplaces · on Paydays · on repos · on socials · on standards · on terms
Changes
Citations rose from 219 to 221.
Citations rose from 209 to 219.
Citations rose from 207 to 209.
Citations rose from 202 to 207.
Citations rose from 200 to 202.
A first count of 9 influential citations.
A first count of 200 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.