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Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding. **Hiring:** We're seeking experienced NLP researchers and intern students focusing on dense retrieval and retrieval-augmented LLMs. If you're interested, please feel free to reach out to us via email at zhengliu1026@gmail.com.

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SegVol

BAAI

The SegVol is a universal and interactive model for volumetric medical image segmentation. SegVol accepts point, box, and text prompts while output volumetric segmentation. By training on 90k unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation model supports the segmentation of over 200 anatomical categories. We have released 25 open source datasets(M3D-Seg) for training SegVol, and these preprocessed data have been uploaded to ModelScope and HuggingFace. You can use the following script to easily load cases and insert them into Test script and Training script. 我们已经发布了用于训练SegVol的25个开源数据集(M3D-Seg),并将预处理后的数据上传到了ModelScope和HuggingFace。 您可以使用下面的script方便地载入，并插入到Test script和Training script中。

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Current early checkpoint is fully functional and outperforms multilingual-e5-large, BGE-M3 and JaColBERT in early results, but full evaluation TBD.# Intro > There is currently no JaColBERTv2 technical report. For an overall idea, you can refer to the JaColBERTv1 arXiv Report Welcome to JaColBERT version 2, the second release of JaColBERT, a Japanese-only document retrieval model based on ColBERT.

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BidirLM is a family of 5 frontier bidirectional encoders, including an omnimodal variant at 2.5B, adapted from causal decoder LLMs. Contrary to contrastive-only models, BidirLM relies on a prior masking phase (MNTP) that enables state-of-the-art results on task-specific fine-tuning (NER, classification, NLI) while achieving frontier performance on embedding benchmarks (MTEB) against open-source alternatives. > *From the paper. MTEB(Multilingual, v2) below matches the paper; the LongEmbed table reports additional long-context results.* (\*) Evaluated at `max_seq_length=512`, matching the paper. The architecture supports much longer context (see the LongEmbed table below); on the MTEB leaderboard the shared LEMBPasskeyRetrieval task is scored at long context, making the leaderboard MTEB(Multilingual, v2) ~0.4 higher than this table.

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stella_en_400M_v5

billatsectorflow

Hi, everyone, thanks for using stella models. After six months of work, I trained the jasper model on top of the stella model, which is a multimodal model, and it can be ranked 2 in mteb (submitted the results on 2024-12-11, which may need official review https://github.com/embeddings-benchmark/results/pull/68). I'll focus on the technical report, training data and related code, hopefully the tricks I've used will be of some help to you guys! The core training code will be integrated into the rag-retrieval library(https://github.com/NLPJCL/RAG-Retrieval) in the near future. (Welcome to star)

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vietnamese-bi-encoder

bkai-foundation-models

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. We train the model on a merged training dataset that consists of: - MS Macro (translated into Vietnamese) - SQuAD v2 (translated into Vietnamese) - 80% of the training set from the Legal Text Retrieval Zalo 2021 challenge We use phobert-base-v2 as the pre-trained backbone.

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