--- title: "bekko-static-v1-slim-100k" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/06cff8ce-17db-496f-820c-7715fb5c7dd3" updated: "2026-11-13" --- # bekko-static-v1-slim-100k > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns text into 1024-dimension vectors with a static embedding model, trimmed to 25 languages. Fast sentence similarity and retrieval, without a transformer, in Python or Rust. bekko-static-v1-slim-100k 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 flowercomputers ## About ### What it is bekko-static-v1-slim-100k is a static embedding model. It looks up each token in a table and averages the results, with no transformer, so text becomes a 1024-dimension vector quickly. It is a trimmed copy of bekko-static-embedding-v1 that keeps half of the 200,000 tokens. ### Who it suits It suits developers who build search or similarity features and want small, fast embeddings across 25 languages, from English and Arabic to Yoruba and Chinese. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search developers | Embed documents and queries for retrieval | Rank help articles against a question | | Research | Retrieval researchers | Test embedding quality against vocabulary size | Compare the family's builds on NanoBEIR | | Embedded systems | Rust developers | Compile the embedder into a program | A search feature with no runtime download | If you need languages outside the 25, you can skip it, since their tokens were removed. ### Where to use it - Python with the sentence-transformers library, on Windows, macOS or Linux - Rust programs, through the encoders crate - Retrieval and similarity jobs over text in the 25 supported languages Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Sentence similarity | Developers | A similarity score between two texts | | Document retrieval | Search teams | Vectors you can rank by closeness | | Smaller vectors | Storage-minded teams | Shorter embeddings by cutting the width | ### How to set it up - Python with sentence-transformers, or a Rust project that uses the encoders crate - No prompts or prefixes are needed for queries or documents 1. Install sentence-transformers if you use Python. 2. Load the model by name in your code. 3. For Rust, add the encoders crate with this model's feature. ```bash pip install -U huggingface_hub hf download flowercomputers/bekko-static-v1-slim-100k ``` ```bash $ cargo add encoders --features='bekko-static-v1-slim-100k' ``` ### How to use it Encode your texts and compare the vectors. Pass a smaller width, such as 256, to get a shorter Matryoshka embedding. In Rust, call the encode functions on one string or a batch. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("flower-computer/bekko-static-v1-slim-100k") # add truncate_dim=256 etc. for Matryoshka sizes emb = model.encode(["What is the capital of France?", "Paris is the capital of France."]) print(model.similarity(emb[0:1], emb[1:2])) ``` ```text let one = encoders::encode_single("What is the capital of France?"); let batch = encoders::encode(["Paris is the capital of France.", "Tokyo is the capital of Japan."]); ``` Rust crate options | Tool | What it does | | --- | --- | | mrl-512 to mrl-32 | Cut the vectors to a Matryoshka width, with 1024 as the default | | int8, int16, float32 | Choose the table type, where int16 is the default | | rayon | Runs batches in parallel | ### Example You encode a question and a sentence that answers it, such as the capital of France. The code prints one similarity score for the pair, and a matching answer should score higher than an unrelated sentence. ### For newcomers and experts If you are new, an embedding turns a sentence into a list of numbers so a program can tell how close two sentences are in meaning. The first small thing to try is the Python snippet with two sentences. If you know the field, the model uses token lookup and mean pooling at 1024 dimensions, with widths from 32 to 1024. The trade-off is quality for size: with this 100k vocabulary, NanoBEIR-en drops from 53.71 to 52.91 and the file halves to 205 MB. Text is split into WordPiece tokens → Each token is looked up in a table → Vectors are averaged into one embedding → Compare embeddings for similarity ### Good to know NanoBEIR-en score by vocabulary size | Label | Value | | --- | --- | | 200k, source model | 53.71 | | 100k, this model | 52.91 | | 75k | 52.09 | | 50k | 50.22 | | 32k | 49.31 | - Languages: text in languages outside the 25 is not supported and falls back to characters or an unknown token. - Size: about 205 MB in F16, against 410 MB for the source model. - License: mit. - Cost to run: no API key, account or paid service is named. - Data it reaches: Python downloads the model from Hugging Face. The Rust crate compiles it in, so there is no runtime download. - Not for: languages outside the 25 it covers. ## README ````markdown # bekko-static-v1-slim-100k Turns text into 1024-dimension vectors with a static embedding model, trimmed to 25 languages. Fast sentence similarity and retrieval, without a transformer, in Python or Rust. Made by flowercomputers 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: bekko-static-v1-slim-100k is a static embedding model. It looks up each token in a table and averages the results, with no transformer, so text becomes a 1024-dimension vector quickly. It is a trimmed copy of bekko-static-embedding-v1 that keeps half of the 200,000 tokens. Who it suits: It suits developers who build search or similarity features and want small, fast embeddings across 25 languages, from English and Arabic to Yoruba and Chinese. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Software | Search developers | Embed documents and queries for retrieval | Rank help articles against a question Research | Retrieval researchers | Test embedding quality against vocabulary size | Compare the family's builds on NanoBEIR Embedded systems | Rust developers | Compile the embedder into a program | A search feature with no runtime download ``` If you need languages outside the 25, you can skip it, since their tokens were removed. Where to use it: - Python with the sentence-transformers library, on Windows, macOS or Linux - Rust programs, through the encoders crate - Retrieval and similarity jobs over text in the 25 supported languages ```table Title: Jobs it does Use case | Who | What they get back Sentence similarity | Developers | A similarity score between two texts Document retrieval | Search teams | Vectors you can rank by closeness Smaller vectors | Storage-minded teams | Shorter embeddings by cutting the width ``` How to set it up: - Python with sentence-transformers, or a Rust project that uses the encoders crate - No prompts or prefixes are needed for queries or documents 1. Install sentence-transformers if you use Python. 2. Load the model by name in your code. 3. For Rust, add the encoders crate with this model's feature. ```bash pip install -U huggingface_hub hf download flowercomputers/bekko-static-v1-slim-100k ``` ```bash $ cargo add encoders --features='bekko-static-v1-slim-100k' ``` How to use it: Encode your texts and compare the vectors. Pass a smaller width, such as 256, to get a shorter Matryoshka embedding. In Rust, call the encode functions on one string or a batch. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("flower-computer/bekko-static-v1-slim-100k") # add truncate_dim=256 etc. for Matryoshka sizes emb = model.encode(["What is the capital of France?", "Paris is the capital of France."]) print(model.similarity(emb[0:1], emb[1:2])) ``` ```text let one = encoders::encode_single("What is the capital of France?"); let batch = encoders::encode(["Paris is the capital of France.", "Tokyo is the capital of Japan."]); ``` ```table Title: Rust crate options Tool | What it does mrl-512 to mrl-32 | Cut the vectors to a Matryoshka width, with 1024 as the default int8, int16, float32 | Choose the table type, where int16 is the default rayon | Runs batches in parallel ``` Example: You encode a question and a sentence that answers it, such as the capital of France. The code prints one similarity score for the pair, and a matching answer should score higher than an unrelated sentence. For newcomers and experts: If you are new, an embedding turns a sentence into a list of numbers so a program can tell how close two sentences are in meaning. The first small thing to try is the Python snippet with two sentences. If you know the field, the model uses token lookup and mean pooling at 1024 dimensions, with widths from 32 to 1024. The trade-off is quality for size: with this 100k vocabulary, NanoBEIR-en drops from 53.71 to 52.91 and the file halves to 205 MB. ```flow Text is split into WordPiece tokens Each token is looked up in a table Vectors are averaged into one embedding Compare embeddings for similarity ``` Good to know: ```chart Title: NanoBEIR-en score by vocabulary size 200k, source model: 53.71 100k, this model: 52.91 75k: 52.09 50k: 50.22 32k: 49.31 ``` - Languages: text in languages outside the 25 is not supported and falls back to characters or an unknown token. - Size: about 205 MB in F16, against 410 MB for the source model. - License: mit. - Cost to run: no API key, account or paid service is named. - Data it reaches: Python downloads the model from Hugging Face. The Rust crate compiles it in, so there is no runtime download. - Not for: languages outside the 25 it covers. ## On Hugging Face, 13 November 2026 - Task: sentence similarity - Library: sentence-transformers - Parameters: 102M - Weights: about 205 MB in F16; about 51 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 0 - Likes: 3 - License: mit - Languages: en, ar, bn, de, es, fa, fi, fr - Based on: hotchpotch/bekko-static-embedding-v1 - Tags: sentence-transformers, safetensors, static-embedding, sentence-similarity, feature-extraction, vocabulary-pruning, multilingual ## Links - Model: https://huggingface.co/flowercomputers/bekko-static-v1-slim-100k - Files: https://huggingface.co/flowercomputers/bekko-static-v1-slim-100k/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download flowercomputers/bekko-static-v1-slim-100k ``` 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 [flowercomputers/bekko-static-v1-slim-100k on Hugging Face](https://huggingface.co/flowercomputers/bekko-static-v1-slim-100k) ## 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 | | --- | --- | --- | --- | --- | --- | | bekko-static-v1-slim-100k (this page) | 0 | 3 | 102M | mit | 2026-11-06 | | [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 | | [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 | | [hs-embed-1](https://mindket.com/p/68203fd5-48b7-49fa-ba02-80ddf5ece256) | 22,412 | 7 | 112M | mit | 2026-11-02 | | [pplx-embed-v2-late-0.6b](https://mindket.com/p/84bcea19-0097-475e-b7a0-084d5f736714) | 2,765 | 71 | 594M | mit | 2026-10-22 | | [mSlideOn](https://mindket.com/p/e92bd39d-a0eb-440c-8a0d-8c22298b51ce) | 706 | 16 | 339M | apache-2.0 | 2026-10-20 | Numbers as Mindket last read them, on 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: 2026-11-06 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open flowercomputers/bekko-static-v1-slim-100k on Hugging Face](https://huggingface.co/flowercomputers/bekko-static-v1-slim-100k) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/06cff8ce-17db-496f-820c-7715fb5c7dd3) - [Its page on Mindket](https://mindket.com/p/06cff8ce-17db-496f-820c-7715fb5c7dd3)