--- title: "bge-reranker-v2-m3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5bc2430e-32f8-4153-8615-2e4e3bad70ea" updated: "2026-11-12" --- # bge-reranker-v2-m3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores how well a passage answers a query so you can reorder search results, across many languages. Takes a question and a document and returns one relevance score. For people building search and retrieval pipelines. bge-reranker-v2-m3 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by BAAI ## About ### What it is bge-reranker-v2-m3 is a multilingual reranker from BAAI, built on the bge-m3 model. It does not turn texts into vectors. It reads a query and a passage together and returns a single score, where a higher number means a closer match. A sigmoid step can squeeze that score into a range from 0 to 1. ### Who it suits It suits developers who already fetch candidate passages for a query and want a smarter final ordering. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Search products | Search engineers | Reorder retrieved results by how well each one fits the query | Putting the best-matching article first | | Question answering | ML engineers | Pick the passage that best answers a question | Choosing between candidate answers | | Internal knowledge bases | Platform teams | Rank the documents an index returns | Sorting policy documents for one query | | Multilingual sites | Content teams | Compare queries and passages across languages | Matching a question with text written in another language | You can skip it if you need embeddings, since this model gives pair scores and no vectors. ### Where to use it - Python projects that use the FlagEmbedding package - Search and retrieval pipelines that already produce a list of candidates - Windows, macOS or Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Rerank search hits | Search teams | A relevance score for every query and passage pair | | Score one pair | Developers | A raw score, or a 0 to 1 value with normalizing turned on | | Compare many pairs | Data teams | A list of scores for a batch of pairs | ### How to set it up You need Python and the FlagEmbedding package. The model files themselves are optional to fetch by hand. - Python on Windows, macOS or Linux - The FlagEmbedding package - About 2.3 GB for the F32 weights, or less with fp16 loading 1. Install FlagEmbedding. 2. Optionally download the model files with the Hugging Face tool. 3. Load the reranker in Python and score your pairs. ```sh pip install -U FlagEmbedding pip install -U huggingface_hub hf download BAAI/bge-reranker-v2-m3 ``` ### How to use it Load the reranker, then pass it a query and a passage, or a list of such pairs. Turn on normalizing if you want a value between 0 and 1. ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) # -5.65234375 # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score score = reranker.compute_score(['query', 'passage'], normalize=True) print(score) # 0.003497010252573502 ``` | Tool | What it does | | --- | --- | | FlagReranker | Loads the reranker model | | compute\_score | Scores one pair or a list of pairs | | normalize=True | Maps each score into the range 0 to 1 | | use\_fp16=True | Runs faster, with a slight loss of quality | ### Example You score the question "what is panda?" against the word hi and against a sentence about the giant panda. The card shows about -8.19 for hi and about 5.26 for the panda sentence, so the second one ranks higher. ### For newcomers and experts If you are new to search, picture a search engine that returns ten pages. The reranker reads each page next to the question and gives it a score, and you sort by that score. A first small try is the two-line score example above. For people who know the field, this is a cross-encoder with 568M parameters, so each candidate needs its own forward pass and cost grows with the candidate list. The family also has gemma-based and MiniCPM layerwise versions for better quality or for choosing how many layers to run. Weight size of bge-reranker-v2-m3 | Label | Value | | --- | --- | | F32 | 2.3 GB | | 4-bit estimate | 284 MB | Your search returns candidate passages → The reranker reads the query with each passage → It returns one relevance score per pair → You sort by score and keep the best ### Good to know Other models in the same family exist, and the card suggests this one for multilingual work and for light, fast inference. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the queries and passages you pass in - Not for: producing embeddings, since it returns similarity scores instead of vectors ## README ````markdown # bge-reranker-v2-m3 Scores how well a passage answers a query so you can reorder search results, across many languages. Takes a question and a document and returns one relevance score. For people building search and retrieval pipelines. Made by BAAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: bge-reranker-v2-m3 is a multilingual reranker from BAAI, built on the bge-m3 model. It does not turn texts into vectors. It reads a query and a passage together and returns a single score, where a higher number means a closer match. A sigmoid step can squeeze that score into a range from 0 to 1. Who it suits: It suits developers who already fetch candidate passages for a query and want a smarter final ordering. ```table Industry | Who uses it | How they use it | Example Search products | Search engineers | Reorder retrieved results by how well each one fits the query | Putting the best-matching article first Question answering | ML engineers | Pick the passage that best answers a question | Choosing between candidate answers Internal knowledge bases | Platform teams | Rank the documents an index returns | Sorting policy documents for one query Multilingual sites | Content teams | Compare queries and passages across languages | Matching a question with text written in another language ``` You can skip it if you need embeddings, since this model gives pair scores and no vectors. Where to use it: - Python projects that use the FlagEmbedding package - Search and retrieval pipelines that already produce a list of candidates - Windows, macOS or Linux with Python ```table Use case | Who | What they get back Rerank search hits | Search teams | A relevance score for every query and passage pair Score one pair | Developers | A raw score, or a 0 to 1 value with normalizing turned on Compare many pairs | Data teams | A list of scores for a batch of pairs ``` How to set it up: You need Python and the FlagEmbedding package. The model files themselves are optional to fetch by hand. - Python on Windows, macOS or Linux - The FlagEmbedding package - About 2.3 GB for the F32 weights, or less with fp16 loading 1. Install FlagEmbedding. 2. Optionally download the model files with the Hugging Face tool. 3. Load the reranker in Python and score your pairs. ```sh pip install -U FlagEmbedding pip install -U huggingface_hub hf download BAAI/bge-reranker-v2-m3 ``` How to use it: Load the reranker, then pass it a query and a passage, or a list of such pairs. Turn on normalizing if you want a value between 0 and 1. ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) # -5.65234375 # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score score = reranker.compute_score(['query', 'passage'], normalize=True) print(score) # 0.003497010252573502 ``` ```table Tool | What it does FlagReranker | Loads the reranker model compute_score | Scores one pair or a list of pairs normalize=True | Maps each score into the range 0 to 1 use_fp16=True | Runs faster, with a slight loss of quality ``` Example: You score the question "what is panda?" against the word hi and against a sentence about the giant panda. The card shows about -8.19 for hi and about 5.26 for the panda sentence, so the second one ranks higher. For newcomers and experts: If you are new to search, picture a search engine that returns ten pages. The reranker reads each page next to the question and gives it a score, and you sort by that score. A first small try is the two-line score example above. For people who know the field, this is a cross-encoder with 568M parameters, so each candidate needs its own forward pass and cost grows with the candidate list. The family also has gemma-based and MiniCPM layerwise versions for better quality or for choosing how many layers to run. ```chart Title: Weight size of bge-reranker-v2-m3 F32: 2.3 GB 4-bit estimate: 284 MB ``` ```flow Your search returns candidate passages The reranker reads the query with each passage It returns one relevance score per pair You sort by score and keep the best ``` Good to know: Other models in the same family exist, and the card suggests this one for multilingual work and for light, fast inference. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the queries and passages you pass in - Not for: producing embeddings, since it returns similarity scores instead of vectors ## On Hugging Face, 13 November 2026 - Task: text classification - Library: sentence-transformers - Parameters: 568M - Weights: about 2.3 GB in F32; about 284 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 14,685,230 - Likes: 1,301 - License: apache-2.0 - Quantized versions: 73 - Fine-tunes: 137 - Tags: sentence-transformers, safetensors, xlm-roberta, text-classification, transformers, text-embeddings-inference, multilingual ## Links - Model: https://huggingface.co/BAAI/bge-reranker-v2-m3 - Files: https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download BAAI/bge-reranker-v2-m3 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [BAAI/bge-reranker-v2-m3 on Hugging Face](https://huggingface.co/BAAI/bge-reranker-v2-m3) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | bge-reranker-v2-m3 (this page) | 14,685,230 | 1,301 | 568M | apache-2.0 | 2024-06-24 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | | [bge-m3](https://mindket.com/p/aa1f891a-fcd9-4f1d-9d61-8d52777d5ff3) | 29,282,167 | 3,989 | — | mit | 2024-07-03 | | [paraphrase-multilingual-MiniLM-L12-v2](https://mindket.com/p/7babf7ff-2f8f-4358-a71b-a36915eab8d5) | 26,540,125 | 1,505 | 118M | apache-2.0 | 2026-01-28 | | [harrier-oss-v1-0.6b](https://mindket.com/p/0d1574e7-3349-4388-b93f-b577056ab30e) | 218,439 | 338 | 596M | mit | 2026-03-30 | | [voyage-4-nano](https://mindket.com/p/2f60302a-0758-4299-acb3-a23b68c318dc) | 90,118 | 152 | 346M | apache-2.0 | 2026-10-22 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · svcrunch · 2026-02-01](https://news.ycombinator.com/item?id=46850187): Tuning Semantic Search on JFMM.net – Joint Fleet Maintenance Manual: Thanks for your interest. The rerankers are external, GoodMem is a unified API layer that calls out to various providers. There's no model running inside the database or the GoodMem server. We support both commercial APIs and self-hosted options: - Cohere (rerank-english-v3.0, etc.) - Voyage AI… - [Bluesky · Pingoru.io · 2026-10-18](https://bsky.app/profile/pingoru.bsky.social/post/3my5vwt5fzy2p): 🟠 Berget AI is reporting a Partial Outage since 15:17 UTC "BAAI/bge-reranker-v2-m3 is down" Affects: BAAI/bge-reranker-v2-m3 Live timeline → https://pingoru.io/providers/berget-ai/incidents/12306689 \#BergetAI \#BergetAIDown - [Bluesky · Pingoru.io · 2026-10-14](https://bsky.app/profile/pingoru.bsky.social/post/3mxsu4qkt5z2x): 🟠 Berget AI is reporting a Partial Outage since 05:46 UTC "BAAI/bge-reranker-v2-m3 is down" Affects: BAAI/bge-reranker-v2-m3 Live timeline → https://pingoru.io/providers/berget-ai/incidents/12295902 \#BergetAI \#BergetAIDown ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-06-24 - First published: 2024-03-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open BAAI/bge-reranker-v2-m3 on Hugging Face](https://huggingface.co/BAAI/bge-reranker-v2-m3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5bc2430e-32f8-4153-8615-2e4e3bad70ea) - [Its page on Mindket](https://mindket.com/p/5bc2430e-32f8-4153-8615-2e4e3bad70ea)