--- title: "bge-base-en-v1.5" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/daaf8719-1018-47cd-99d3-298e77acfad4" updated: "2026-11-13" --- # bge-base-en-v1.5 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English text into number vectors for search and retrieval. A 109M-parameter embedding model from BAAI, for developers who build semantic search or retrieval for LLM apps through sentence-transformers. bge-base-en-v1.5 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by BAAI ## About ### What it is bge-base-en-v1.5 is an English text embedding model from BAAI. It reads a piece of text and returns a vector of numbers, which you compare with other vectors to find related texts. It has 109M parameters and is used through the sentence-transformers library. The 1.5 release was made to ease the similarity distribution issue and to retrieve better without an instruction. ### Who it suits Developers who build search over their own documents fit best, along with people who give an LLM relevant passages before it answers. Researchers who fine-tune embedding models can start from it too. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers building search | Embed documents and queries, then match them by vector closeness | Finding the help pages closest to a user's question | | AI apps | LLM app builders | Fetch passages to hand to a chat model as context | Pulling product notes into a reply | | Research | NLP researchers | Fine-tune or compare embedding models from a known base | Starting a domain-specific version from this model | You can skip it if your texts are not in English. ### Where to use it It runs in Python through sentence-transformers. - Windows, macOS or Linux, where the files download through Python - Python scripts and notebooks that load models with sentence-transformers - Langchain, which the card says integrates the BGE models - Search setups that pair an embedding model with a reranker | Use case | Who | What they get back | | --- | --- | --- | | Semantic search | Developers | Vectors to compare, so close meanings rank first | | Retrieval for LLMs | App builders | Relevant passages to place in a prompt | | Fine-tuning base | Researchers | A small English embedding model to adapt | ### How to set it up You need Python and a little disk space. - Python on Windows, macOS or Linux - The sentence-transformers library - About 438 MB of disk for the F32 weights, or about 55 MB at 4-bit, which is an estimate 1. Install the Hugging Face hub library. 2. Download the model files with the hf command. 3. Install sentence-transformers and load the model from the downloaded folder. ```sh pip install -U huggingface_hub hf download BAAI/bge-base-en-v1.5 ``` ### How to use it The part of the card available here shows no run command. In practice you load the model with sentence-transformers, embed your texts, and compare the vectors. The table lists related pieces the card names. | Tool | What it does | | --- | --- | | bge-m3 | A sibling model that covers other languages, long inputs and extra retrieval modes | | BAAI/bge-reranker-base | A cross-encoder that re-ranks the top documents an embedding model returns | | BAAI/bge-reranker-large | The larger cross-encoder reranker for the same job | | Langchain | A framework the BGE models are integrated into | ### Example You turn a few support articles and one customer question into vectors with this model. You compare them and keep the articles whose vectors sit closest to the question. ### For newcomers and experts An embedding is a list of numbers that stands for the meaning of a text, and texts with similar meaning get lists that sit close together. The first small thing to try is embedding two short sentences and comparing them. This model is a bi-encoder, so you embed documents once and compare queries against them. The card recommends adding a cross-encoder reranker over the top results when you want better ordering. At 109M parameters it is small enough to run on modest hardware, and the card's v1.5 notes say retrieval works without a query instruction. The record counts 508 fine-tuned versions by other people. Download the files with hf → Load the model in sentence-transformers → Embed your documents and the query → Compare the vectors for closeness → Re-rank the top hits with a reranker ### Good to know The model covers English only, and the card sets it inside the larger FlagEmbedding project. - Other versions: the record counts 31 quantized and 508 fine-tuned versions made by other people. - Reranking: the card suggests re-ranking the top documents with its cross-encoder models. - Task: the record lists feature extraction. Weight size by precision | Label | Value | | --- | --- | | F32 | 438 MB | | 4-bit (estimate) | 55 MB | - License: MIT. - Cost to run: none named, no API key, account or paid service appears in the source. - Data it reaches: it downloads files from Hugging Face, and the only text it reads is the text you embed. - Not for: other languages, long inputs and extra retrieval modes, which the card hands to bge-m3. ## README ````markdown # bge-base-en-v1.5 Turns English text into number vectors for search and retrieval. A 109M-parameter embedding model from BAAI, for developers who build semantic search or retrieval for LLM apps through sentence-transformers. 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-base-en-v1.5 is an English text embedding model from BAAI. It reads a piece of text and returns a vector of numbers, which you compare with other vectors to find related texts. It has 109M parameters and is used through the sentence-transformers library. The 1.5 release was made to ease the similarity distribution issue and to retrieve better without an instruction. Who it suits: Developers who build search over their own documents fit best, along with people who give an LLM relevant passages before it answers. Researchers who fine-tune embedding models can start from it too. ```table Industry | Who uses it | How they use it | Example Software | Developers building search | Embed documents and queries, then match them by vector closeness | Finding the help pages closest to a user's question AI apps | LLM app builders | Fetch passages to hand to a chat model as context | Pulling product notes into a reply Research | NLP researchers | Fine-tune or compare embedding models from a known base | Starting a domain-specific version from this model ``` You can skip it if your texts are not in English. Where to use it: It runs in Python through sentence-transformers. - Windows, macOS or Linux, where the files download through Python - Python scripts and notebooks that load models with sentence-transformers - Langchain, which the card says integrates the BGE models - Search setups that pair an embedding model with a reranker ```table Use case | Who | What they get back Semantic search | Developers | Vectors to compare, so close meanings rank first Retrieval for LLMs | App builders | Relevant passages to place in a prompt Fine-tuning base | Researchers | A small English embedding model to adapt ``` How to set it up: You need Python and a little disk space. - Python on Windows, macOS or Linux - The sentence-transformers library - About 438 MB of disk for the F32 weights, or about 55 MB at 4-bit, which is an estimate 1. Install the Hugging Face hub library. 2. Download the model files with the hf command. 3. Install sentence-transformers and load the model from the downloaded folder. ```sh pip install -U huggingface_hub hf download BAAI/bge-base-en-v1.5 ``` How to use it: The part of the card available here shows no run command. In practice you load the model with sentence-transformers, embed your texts, and compare the vectors. The table lists related pieces the card names. ```table Tool | What it does bge-m3 | A sibling model that covers other languages, long inputs and extra retrieval modes BAAI/bge-reranker-base | A cross-encoder that re-ranks the top documents an embedding model returns BAAI/bge-reranker-large | The larger cross-encoder reranker for the same job Langchain | A framework the BGE models are integrated into ``` Example: You turn a few support articles and one customer question into vectors with this model. You compare them and keep the articles whose vectors sit closest to the question. For newcomers and experts: An embedding is a list of numbers that stands for the meaning of a text, and texts with similar meaning get lists that sit close together. The first small thing to try is embedding two short sentences and comparing them. This model is a bi-encoder, so you embed documents once and compare queries against them. The card recommends adding a cross-encoder reranker over the top results when you want better ordering. At 109M parameters it is small enough to run on modest hardware, and the card's v1.5 notes say retrieval works without a query instruction. The record counts 508 fine-tuned versions by other people. ```flow Download the files with hf Load the model in sentence-transformers Embed your documents and the query Compare the vectors for closeness Re-rank the top hits with a reranker ``` Good to know: The model covers English only, and the card sets it inside the larger FlagEmbedding project. - Other versions: the record counts 31 quantized and 508 fine-tuned versions made by other people. - Reranking: the card suggests re-ranking the top documents with its cross-encoder models. - Task: the record lists feature extraction. ```chart Title: Weight size by precision F32: 438 MB 4-bit (estimate): 55 MB ``` - License: MIT. - Cost to run: none named, no API key, account or paid service appears in the source. - Data it reaches: it downloads files from Hugging Face, and the only text it reads is the text you embed. - Not for: other languages, long inputs and extra retrieval modes, which the card hands to bge-m3. ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: sentence-transformers - Parameters: 109M - Weights: about 438 MB in F32; about 55 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,508,165 - Likes: 542 - License: mit - Languages: en - Quantized versions: 31 - Fine-tunes: 508 - Tags: sentence-transformers, pytorch, onnx, safetensors, bert, feature-extraction, sentence-similarity, transformers, mteb, model-index, text-embeddings-inference ## Links - Model: https://huggingface.co/BAAI/bge-base-en-v1.5 - Files: https://huggingface.co/BAAI/bge-base-en-v1.5/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download BAAI/bge-base-en-v1.5 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [BAAI/bge-base-en-v1.5 on Hugging Face](https://huggingface.co/BAAI/bge-base-en-v1.5) ## 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-base-en-v1.5 (this page) | 8,508,165 | 542 | 109M | mit | 2024-02-21 | | [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | | [nomic-embed-text-v1.5](https://mindket.com/p/55b00ba5-f156-4e3c-93ee-bf7cc7611725) | 11,809,681 | 994 | 137M | apache-2.0 | 2026-04-07 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | | [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 | 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 · navar · 2026-01-17](https://news.ycombinator.com/item?id=46655968): Ask HN: How are you doing RAG locally?: As far as I can tell it should be possible to reuse this fine tuning code entirely and just replace \`--embedder\_name\_or\_path BAAI/bge-base-en-v1.5\` with \`--embedder\_name\_or\_path MongoDB/mdbr-leaf-ir\` Note that bge-base-en-v1.5 is a 110M params model - our is 23M. \* BEIR performance is bge=53.23 vs… - [Hacker News · minimaxir · 2025-11-28](https://news.ycombinator.com/item?id=46081800): 28M Hacker News comments as vector embedding search dataset: Don't use all-MiniLM-L6-v2 for new vector embeddings datasets. Yes, it's the open-weights embedding model used in all the tutorials and it was the most pragmatic model to use in sentence-transformers when vector stores were in their infancy, but it's old and does not implement the newest advances… - [Hacker News · coder68 · 2025-08-27](https://news.ycombinator.com/item?id=45035858): SpaCy: Industrial-Strength Natural Language Processing (NLP) in Python: Ah yes this does make sense. We are definitely in agreement on the point of "wildly inefficient and subpar". I'll try out decoder model embeddings soon, e.g. Qwen/Qwen3-Embedding-8B. I'm working with largish amounts of data (200M records), so I tried to pick a good balance between size:perf:cost,… - [Hacker News · PaulHoule · 2024-05-10](https://news.ycombinator.com/item?id=40318781): Show HN: Exploring HN by mapping and analyzing 40M posts and comments for fun: Some of the SBERT models now are based on T5 and newer architectures so there's not. The FlagEmbedding model that the author uses https://huggingface.co/BAAI/bge-base-en-v1.5 is described as an "LLM" by the people who created it. It can be used in the SBERT framework. I tried quite a few models… - [Hacker News · simonw · 2023-11-26](https://news.ycombinator.com/item?id=38421282): VectorDB: Vector Database Built by Kagi Search: https://github.com/kagisearch/vectordb/blob/453bb658bb710838... Looks like it uses one of these, depending on your settings: Fast model: google/universal-sentence-encoder/4 Multilingual model: universal-sentence-encoder-multilingual-large/3 Normal model (Alternative): BAAI/bge-small-en-v1.5 Best… - [Hacker News · caesil · 2023-09-28](https://news.ycombinator.com/item?id=37693524): Workers AI: Serverless GPU-powered inference: This is just a press release, and not a great source of in-depth info. This blog post from yesterday goes into it: https://blog.cloudflare.com/workers-ai/ \>Models you know and love \>We’re launching with a curated set of popular, open source models, that cover a wide range of inference tasks: \>Text… ## 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: 2024-02-21 - First published: 2023-09-11 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open BAAI/bge-base-en-v1.5 on Hugging Face](https://huggingface.co/BAAI/bge-base-en-v1.5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/daaf8719-1018-47cd-99d3-298e77acfad4) - [Its page on Mindket](https://mindket.com/p/daaf8719-1018-47cd-99d3-298e77acfad4)