--- title: "mine-embed-v1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c0798dc1-534c-4be5-81ee-25652120c259" updated: "2026-11-12" --- # mine-embed-v1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores how close two short English texts are in meaning with a small 57.8M embedding model trained from scratch, an early experiment for people who study or baseline embedding training, not for production search. mine-embed-v1 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 MineAITechnology ## About ### What it is mine-embed-v1 is a small English text embedding model from MineAI Technology with 57.8M parameters. It turns a sentence into a vector of 512 numbers, and the cosine similarity between two vectors tells you how close the texts are in meaning. The authors call it an early experiment trained from scratch, and they do not present it as competitive or production-ready. ### Who it suits It suits researchers and students who study small-scale embedding training, or who want a baseline to compare later models against. Who uses this model | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Embedding researchers | Use it as a reference point for later versions | Compare its scores with other small embedding models | | Education | Students and learners | Study how an embedding model is trained from scratch | Read the training stages and run the sample | | Software | Developers | Run quick similarity experiments on short English texts | Score how close two sentences are | If you need search, clustering or anything in production, you can skip it, because the card advises against those uses. ### Where to use it - Python scripts with torch - Windows, macOS or Linux with Python Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Sentence similarity | Researchers | Cosine scores between short English texts | | Baselines | Embedding researchers | A weak reference model to compare against | | Training study | Students | A small model whose training notes are public | ### How to set it up You need Python and a few packages. The repository carries its own model.py, so you load it through its Embedder class and not through AutoModel or SentenceTransformer. - Python with pip - torch, safetensors and huggingface\_hub - transformers, used only for the tokenizer - A Hugging Face token, if the repository is private 1. Install the packages listed above. 2. Download the repository and add its folder to your Python path. 3. Import Embedder and encode your sentences. ```bash pip install -U huggingface_hub hf download MineAITechnology/mine-embed-v1 ``` ### How to use it Download the folder, import Embedder from its model file, and call encode with a list of sentences. The result is already normalized, so multiplying the matrix by its transpose gives cosine similarities. Inputs are cut to 128 tokens by default, and no query or passage prefixes are used. ```python import sys from huggingface_hub import snapshot_download # private repo: log in with an HF token first folder = snapshot_download("MineAITechnology/mine-embed-v1") sys.path.insert(0, folder) from model import Embedder e = Embedder(folder) # loads config.json + model.safetensors, GPT-2 tokenizer vecs = e.encode(["A man is playing a guitar.", "Someone strums an instrument.", "Stocks fell today."]) print(vecs.shape) # torch.Size([3, 512]), already L2-normalised print(vecs @ vecs.T) # cosine similarities ``` Main parts of the sample | Tool | What it does | | --- | --- | | snapshot\_download | Fetches the repository folder from Hugging Face | | Embedder | Loads the config, weights and tokenizer from that folder | | encode | Turns a list of sentences into normalized vectors | ### Example You encode three sentences, two about someone playing a guitar and one about stocks falling. You get a 3 by 512 array of vectors, and the matrix product shows how close each pair of sentences is. ### For newcomers and experts An embedding is a list of numbers that stands for a sentence's meaning, so similar sentences get similar lists. A first small test is the sample script with three sentences of your own. Treat the scores as experimental. The encoder has 10 layers, hidden size 512 and 8 heads, with mean pooling and L2 normalization on the output. It uses the GPT-2 byte-pair tokenizer, which is weak outside English. Training had two stages: masked language modelling on a corpus that is largely synthetic, then contrastive fine-tuning on 471,400 triplets, mostly AllNLI plus Quora duplicates. Random initialization → Masked language modelling → Contrastive fine-tuning → Sentence in, 512-number vector out ### Good to know The card says that on a 28-task MTEB subset the model scores well below standard small embedding models. It also says ordinary English text makes up little of the training corpus. Reference models, MTEB 28-task average | Label | Value | | --- | --- | | bge-base-en-v1.5 | 66.25 points | | bge-small-en-v1.5 | 64.37 points | | gte-small | 63.95 points | | all-mpnet-base-v2 | 62.66 points | | all-MiniLM-L12-v2 | 61.6 points | - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the text you encode stays on your own computer. - Not for: finding documents, grouping texts or running a live search service, which the card does not recommend. ## README ````markdown # mine-embed-v1 Scores how close two short English texts are in meaning with a small 57.8M embedding model trained from scratch, an early experiment for people who study or baseline embedding training, not for production search. Made by MineAITechnology 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: mine-embed-v1 is a small English text embedding model from MineAI Technology with 57.8M parameters. It turns a sentence into a vector of 512 numbers, and the cosine similarity between two vectors tells you how close the texts are in meaning. The authors call it an early experiment trained from scratch, and they do not present it as competitive or production-ready. Who it suits: It suits researchers and students who study small-scale embedding training, or who want a baseline to compare later models against. ```table Title: Who uses this model Industry | Who uses it | How they use it | Example Research | Embedding researchers | Use it as a reference point for later versions | Compare its scores with other small embedding models Education | Students and learners | Study how an embedding model is trained from scratch | Read the training stages and run the sample Software | Developers | Run quick similarity experiments on short English texts | Score how close two sentences are ``` If you need search, clustering or anything in production, you can skip it, because the card advises against those uses. Where to use it: - Python scripts with torch - Windows, macOS or Linux with Python ```table Title: Jobs it does Use case | Who | What they get back Sentence similarity | Researchers | Cosine scores between short English texts Baselines | Embedding researchers | A weak reference model to compare against Training study | Students | A small model whose training notes are public ``` How to set it up: You need Python and a few packages. The repository carries its own model.py, so you load it through its Embedder class and not through AutoModel or SentenceTransformer. - Python with pip - torch, safetensors and huggingface_hub - transformers, used only for the tokenizer - A Hugging Face token, if the repository is private 1. Install the packages listed above. 2. Download the repository and add its folder to your Python path. 3. Import Embedder and encode your sentences. ```bash pip install -U huggingface_hub hf download MineAITechnology/mine-embed-v1 ``` How to use it: Download the folder, import Embedder from its model file, and call encode with a list of sentences. The result is already normalized, so multiplying the matrix by its transpose gives cosine similarities. Inputs are cut to 128 tokens by default, and no query or passage prefixes are used. ```python import sys from huggingface_hub import snapshot_download # private repo: log in with an HF token first folder = snapshot_download("MineAITechnology/mine-embed-v1") sys.path.insert(0, folder) from model import Embedder e = Embedder(folder) # loads config.json + model.safetensors, GPT-2 tokenizer vecs = e.encode(["A man is playing a guitar.", "Someone strums an instrument.", "Stocks fell today."]) print(vecs.shape) # torch.Size([3, 512]), already L2-normalised print(vecs @ vecs.T) # cosine similarities ``` ```table Title: Main parts of the sample Tool | What it does snapshot_download | Fetches the repository folder from Hugging Face Embedder | Loads the config, weights and tokenizer from that folder encode | Turns a list of sentences into normalized vectors ``` Example: You encode three sentences, two about someone playing a guitar and one about stocks falling. You get a 3 by 512 array of vectors, and the matrix product shows how close each pair of sentences is. For newcomers and experts: An embedding is a list of numbers that stands for a sentence's meaning, so similar sentences get similar lists. A first small test is the sample script with three sentences of your own. Treat the scores as experimental. The encoder has 10 layers, hidden size 512 and 8 heads, with mean pooling and L2 normalization on the output. It uses the GPT-2 byte-pair tokenizer, which is weak outside English. Training had two stages: masked language modelling on a corpus that is largely synthetic, then contrastive fine-tuning on 471,400 triplets, mostly AllNLI plus Quora duplicates. ```flow Random initialization Masked language modelling Contrastive fine-tuning Sentence in, 512-number vector out ``` Good to know: The card says that on a 28-task MTEB subset the model scores well below standard small embedding models. It also says ordinary English text makes up little of the training corpus. ```chart Title: Reference models, MTEB 28-task average bge-base-en-v1.5: 66.25 points bge-small-en-v1.5: 64.37 points gte-small: 63.95 points all-mpnet-base-v2: 62.66 points all-MiniLM-L12-v2: 61.6 points ``` - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the text you encode stays on your own computer. - Not for: finding documents, grouping texts or running a live search service, which the card does not recommend. ## On Hugging Face, 13 November 2026 - Task: sentence similarity - Library: pytorch - Parameters: 57.8M - Weights: about 231 MB in F32; about 29 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 77 - Likes: 6 - License: apache-2.0 - Languages: en - Tags: pytorch, safetensors, embeddings, sentence-embeddings, encoder, custom-architecture, experimental, sentence-similarity ## Links - Model: https://huggingface.co/MineAITechnology/mine-embed-v1 - Files: https://huggingface.co/MineAITechnology/mine-embed-v1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download MineAITechnology/mine-embed-v1 ``` 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 [MineAITechnology/mine-embed-v1 on Hugging Face](https://huggingface.co/MineAITechnology/mine-embed-v1) ## 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 | | --- | --- | --- | --- | --- | --- | | mine-embed-v1 (this page) | 77 | 6 | 57.8M | apache-2.0 | 2026-10-17 | | [hs-embed-1](https://mindket.com/p/68203fd5-48b7-49fa-ba02-80ddf5ece256) | 22,412 | 7 | 112M | mit | 2026-11-02 | | [spkrec-ecapa-voxceleb](https://mindket.com/p/7d50056c-46cf-40c2-9b77-eddf0e1ba5ca) | 1,912,322 | 370 | — | apache-2.0 | 2025-02-18 | | [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 | 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: 2026-10-17 - First published: 2026-10-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open MineAITechnology/mine-embed-v1 on Hugging Face](https://huggingface.co/MineAITechnology/mine-embed-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c0798dc1-534c-4be5-81ee-25652120c259) - [Its page on Mindket](https://mindket.com/p/c0798dc1-534c-4be5-81ee-25652120c259)