--- title: "bge-small-zh-v1.5" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/83e03537-e5c9-454f-af04-4f4f95308499" updated: "2026-11-13" --- # bge-small-zh-v1.5 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns Chinese text into dense vectors for search, classification, clustering and semantic matching. A 24M-parameter BGE embedding model from BAAI under the MIT license, about 96 MB in F32. bge-small-zh-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-small-zh-v1.5 is a small Chinese text embedding model from BAAI, part of the FlagEmbedding project. It maps a piece of text to a short list of numbers, called a dense vector. These vectors serve retrieval, classification, clustering and semantic search, and they can sit in the vector databases that LLM apps use. Version 1.5 was released to give a more reasonable spread of similarity scores and better retrieval without an instruction prefix. ### Who it suits It suits developers who build search or retrieval over Chinese text and want a model that is small to download and quick to run. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Search | Teams building document search | Embed documents and queries, then compare the vectors | Find Chinese passages related to a question | | Customer support | Teams matching questions to answers | Embed saved answers and look up the closest one | Match a customer's question to a stored reply | | AI apps | Developers building retrieval for LLMs | Store the vectors in a vector database | Fetch relevant Chinese notes before asking a model | You can skip it if your text is not Chinese, since Hugging Face lists only Chinese for this model. ### Where to use it - Python scripts that use the transformers library - Vector databases and LLM tools such as Langchain, which the card says works with BGE models - Windows, macOS and Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Semantic search | Search developers | Vectors whose closeness reflects similar meaning | | Clustering | Data analysts | Vectors that group similar texts together | | Classification | Machine learning engineers | Features to train a small classifier on | ### How to set it up You need these first: - Python on Windows, macOS or Linux - The libraries named in the card's usage section - About 96 MB of disk space for the F32 weights 1. Install or update the Hugging Face download client. 2. Download the model files to your computer. 3. Follow the usage section of the model card to load the model. 4. Embed a few Chinese sentences and compare the vectors. ```sh pip install -U huggingface_hub hf download BAAI/bge-small-zh-v1.5 ``` ### How to use it You pass Chinese sentences to the model and get one vector for each. Closer vectors mean closer meaning. The text available here cuts off before the usage section, so follow the full card for code and for whether a query prefix helps. | Tool | What it does | | --- | --- | | FlagEmbedding | The BAAI project that holds this model's code, usage and fine tuning | | bge-reranker models | Cross-encoders that re-rank the top results from an embedding model, more accurate but slower | | C-MTEB | A Chinese benchmark of 31 test datasets for embedding models | | Langchain | A framework the card says has BGE models built in | | Query instruction | An optional prefix for retrieval queries, listed in the card's model table | ### Example You embed three saved Chinese answers and one customer question. You compare the question's vector with each answer's vector and take the closest answer as the best match. ### For newcomers and experts An embedding turns a sentence into numbers so that similar meanings land close together. A good first try is embedding two related sentences and one unrelated sentence, then checking which pair is closest. For readers who know the field, this is a dense embedding model, and the card says its reranker models are more accurate but less efficient. The card advises using a reranker to reorder the top results that an embedding model returns. Version 1.5 targets the similarity distribution issue of the first release. Chinese text goes in → The model turns it into a vector → Vectors are compared or stored → Closest matches are returned → A reranker can reorder the top results ### Good to know - Cut: the text available here ends inside the card's model table, before usage and evaluation. - Version: the 1.5 models aim for a more reasonable similarity distribution. - Reranking: the card recommends a reranker to reorder the top results from an embedding model. - Language: Hugging Face lists Chinese. - Size: 24M parameters, about 96 MB in F32. - Versions: Hugging Face lists 11 quantized and 12 fine tuned versions made by others. - License: MIT. - Cost to run: none named. The weights are about 96 MB in F32. - Data it reaches: the text you embed, on your own machine. The text available here names no network use. - Not for: text in other languages, since the card lists separate English models. ## README ````markdown # bge-small-zh-v1.5 Turns Chinese text into dense vectors for search, classification, clustering and semantic matching. A 24M-parameter BGE embedding model from BAAI under the MIT license, about 96 MB in F32. 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-small-zh-v1.5 is a small Chinese text embedding model from BAAI, part of the FlagEmbedding project. It maps a piece of text to a short list of numbers, called a dense vector. These vectors serve retrieval, classification, clustering and semantic search, and they can sit in the vector databases that LLM apps use. Version 1.5 was released to give a more reasonable spread of similarity scores and better retrieval without an instruction prefix. Who it suits: It suits developers who build search or retrieval over Chinese text and want a model that is small to download and quick to run. ```table Industry | Who uses it | How they use it | Example Search | Teams building document search | Embed documents and queries, then compare the vectors | Find Chinese passages related to a question Customer support | Teams matching questions to answers | Embed saved answers and look up the closest one | Match a customer's question to a stored reply AI apps | Developers building retrieval for LLMs | Store the vectors in a vector database | Fetch relevant Chinese notes before asking a model ``` You can skip it if your text is not Chinese, since Hugging Face lists only Chinese for this model. Where to use it: - Python scripts that use the transformers library - Vector databases and LLM tools such as Langchain, which the card says works with BGE models - Windows, macOS and Linux with Python ```table Use case | Who | What they get back Semantic search | Search developers | Vectors whose closeness reflects similar meaning Clustering | Data analysts | Vectors that group similar texts together Classification | Machine learning engineers | Features to train a small classifier on ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - The libraries named in the card's usage section - About 96 MB of disk space for the F32 weights 1. Install or update the Hugging Face download client. 2. Download the model files to your computer. 3. Follow the usage section of the model card to load the model. 4. Embed a few Chinese sentences and compare the vectors. ```sh pip install -U huggingface_hub hf download BAAI/bge-small-zh-v1.5 ``` How to use it: You pass Chinese sentences to the model and get one vector for each. Closer vectors mean closer meaning. The text available here cuts off before the usage section, so follow the full card for code and for whether a query prefix helps. ```table Tool | What it does FlagEmbedding | The BAAI project that holds this model's code, usage and fine tuning bge-reranker models | Cross-encoders that re-rank the top results from an embedding model, more accurate but slower C-MTEB | A Chinese benchmark of 31 test datasets for embedding models Langchain | A framework the card says has BGE models built in Query instruction | An optional prefix for retrieval queries, listed in the card's model table ``` Example: You embed three saved Chinese answers and one customer question. You compare the question's vector with each answer's vector and take the closest answer as the best match. For newcomers and experts: An embedding turns a sentence into numbers so that similar meanings land close together. A good first try is embedding two related sentences and one unrelated sentence, then checking which pair is closest. For readers who know the field, this is a dense embedding model, and the card says its reranker models are more accurate but less efficient. The card advises using a reranker to reorder the top results that an embedding model returns. Version 1.5 targets the similarity distribution issue of the first release. ```flow Chinese text goes in The model turns it into a vector Vectors are compared or stored Closest matches are returned A reranker can reorder the top results ``` Good to know: - Cut: the text available here ends inside the card's model table, before usage and evaluation. - Version: the 1.5 models aim for a more reasonable similarity distribution. - Reranking: the card recommends a reranker to reorder the top results from an embedding model. - Language: Hugging Face lists Chinese. - Size: 24M parameters, about 96 MB in F32. - Versions: Hugging Face lists 11 quantized and 12 fine tuned versions made by others. - License: MIT. - Cost to run: none named. The weights are about 96 MB in F32. - Data it reaches: the text you embed, on your own machine. The text available here names no network use. - Not for: text in other languages, since the card lists separate English models. ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: transformers - Parameters: 24M - Weights: about 96 MB in F32; about 12 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,944,941 - Likes: 171 - License: mit - Languages: zh - Quantized versions: 11 - Fine-tunes: 12 - Tags: transformers, pytorch, safetensors, bert, feature-extraction, text-embeddings-inference ## Links - Model: https://huggingface.co/BAAI/bge-small-zh-v1.5 - Files: https://huggingface.co/BAAI/bge-small-zh-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-small-zh-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-small-zh-v1.5 on Hugging Face](https://huggingface.co/BAAI/bge-small-zh-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-small-zh-v1.5 (this page) | 2,944,941 | 171 | 24M | mit | 2023-10-12 | | [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 | | [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 | | [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 | | [bge-base-en-v1.5](https://mindket.com/p/daaf8719-1018-47cd-99d3-298e77acfad4) | 8,508,165 | 542 | 109M | mit | 2024-02-21 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2023-10-12 - First published: 2023-09-12 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open BAAI/bge-small-zh-v1.5 on Hugging Face](https://huggingface.co/BAAI/bge-small-zh-v1.5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/83e03537-e5c9-454f-af04-4f4f95308499) - [Its page on Mindket](https://mindket.com/p/83e03537-e5c9-454f-af04-4f4f95308499)