--- title: "Ru-Jev" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/53220f2d-669f-4526-ad1e-0c53c5d2ca0f" updated: "2026-11-14" --- # Ru-Jev > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores a Russian-first decision from a context and a list of options, giving a probability for yes/no, choice or rating questions. Runs on a plain CPU, for teams that keep data in-house. Ru-Jev is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-14. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by besktech ## About ### What it is Ru-Jev is a Russian-first model that makes typed decisions instead of writing text. You give it a context and a list of options, and it scores every option with a probability and picks the likeliest. It handles yes/no questions, a choice from options and ratings on an ordinal scale. ### Who it suits It suits developers and data teams who need to route or classify Russian text, want a probability they can read, and work on a machine without a GPU or on a closed network. Who uses it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Backend developers | Route incoming messages to a category | Pick weather from a list of 18 categories | | Customer support | Support tooling teams | Sort Russian-language requests into fixed queues | Choose the right queue for a message | | Closed networks | Platform engineers | Run the model on CPU inside a private perimeter | Score options with no outside service | | Research | NLP researchers | Check how accuracy changes when a label is renamed | Compare a Russian word with a jargon wording | Anyone who needs free-form text generation can skip it, as can teams who work in languages other than Russian and English. ### Where to use it - Python with the transformers library - Windows, macOS or Linux with Python installed - A CPU-only machine, no GPU needed - A closed internal network Top jobs | Use case | Who | What they get back | | --- | --- | --- | | Routing a message | Developers | A category from a fixed list, with its probability | | Yes or no questions | Analysts | A probability for yes and one for no | | Rating on a scale | Product teams | A probability for each level of the scale | ### How to set it up - Python on your computer - The huggingface\_hub package - About 1.2 GB of disk for the full-precision weights 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Open the model card and copy its loading snippet. Do not use the stock zero-shot pipeline. ```sh pip install -U huggingface_hub hf download besktech/ru-jev ``` ### How to use it Load the model with the snippet from its card, pass a context and your list of options, and read the probabilities it returns. Earlier releases sit on branches, and you pick one at load time with this argument. ```text revision="v1.1" ``` ### Example You pass a short Russian message about tomorrow's rain together with a list of categories that includes weather. Back comes a probability for each category, with weather on top. On its own example the card reports 0.95 for the right option, where the stock zero-shot pipeline gave at most 0.44. ### For newcomers and experts Think of it as a sorter that reads a sentence and a list of choices, then tells you how sure it is about each one. The first small thing to try is a yes or no question in Russian, loaded the card's way. The stock pipeline returns random numbers, so avoid it. Under the hood it is an mmBERT-base encoder with 307M parameters and a separate scoring head stored in head.safetensors. Stock pipelines load through AutoModelForSequenceClassification, which quietly starts a fresh head. The head begins from random weights and trains together with the backbone on open datasets with human labels. Version 1.3 trains each fixed option set in several wordings, so the model reads the option text instead of memorising it. Context and options go in → The mmBERT-base encoder reads both → A separate head scores each option → Probabilities come out, top option picked ### Good to know - Limits: The inference widget and the stock pipelines do not work with it, so load it the card's way. - Weak spots: Opaque codes and short abbreviations as labels score poorly, and accuracy falls as the candidate list grows. - License: MIT. - Cost to run: none named, and it runs on CPU. - Data it reaches: it downloads its files from Hugging Face, and the source names nothing else. - Not for: generating text. It is not affiliated with TypeSafe AI. Accuracy on jargon labels with 5 candidates | Label | Value | | --- | --- | | v1.1 | 0.633 accuracy | | v1.3 | 0.855 accuracy | ## README ````markdown # Ru-Jev Scores a Russian-first decision from a context and a list of options, giving a probability for yes/no, choice or rating questions. Runs on a plain CPU, for teams that keep data in-house. Made by besktech 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: Ru-Jev is a Russian-first model that makes typed decisions instead of writing text. You give it a context and a list of options, and it scores every option with a probability and picks the likeliest. It handles yes/no questions, a choice from options and ratings on an ordinal scale. Who it suits: It suits developers and data teams who need to route or classify Russian text, want a probability they can read, and work on a machine without a GPU or on a closed network. ```table Title: Who uses it Industry | Who uses it | How they use it | Example Software | Backend developers | Route incoming messages to a category | Pick weather from a list of 18 categories Customer support | Support tooling teams | Sort Russian-language requests into fixed queues | Choose the right queue for a message Closed networks | Platform engineers | Run the model on CPU inside a private perimeter | Score options with no outside service Research | NLP researchers | Check how accuracy changes when a label is renamed | Compare a Russian word with a jargon wording ``` Anyone who needs free-form text generation can skip it, as can teams who work in languages other than Russian and English. Where to use it: - Python with the transformers library - Windows, macOS or Linux with Python installed - A CPU-only machine, no GPU needed - A closed internal network ```table Title: Top jobs Use case | Who | What they get back Routing a message | Developers | A category from a fixed list, with its probability Yes or no questions | Analysts | A probability for yes and one for no Rating on a scale | Product teams | A probability for each level of the scale ``` How to set it up: - Python on your computer - The huggingface_hub package - About 1.2 GB of disk for the full-precision weights 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Open the model card and copy its loading snippet. Do not use the stock zero-shot pipeline. ```sh pip install -U huggingface_hub hf download besktech/ru-jev ``` How to use it: Load the model with the snippet from its card, pass a context and your list of options, and read the probabilities it returns. Earlier releases sit on branches, and you pick one at load time with this argument. ```text revision="v1.1" ``` Example: You pass a short Russian message about tomorrow's rain together with a list of categories that includes weather. Back comes a probability for each category, with weather on top. On its own example the card reports 0.95 for the right option, where the stock zero-shot pipeline gave at most 0.44. For newcomers and experts: Think of it as a sorter that reads a sentence and a list of choices, then tells you how sure it is about each one. The first small thing to try is a yes or no question in Russian, loaded the card's way. The stock pipeline returns random numbers, so avoid it. Under the hood it is an mmBERT-base encoder with 307M parameters and a separate scoring head stored in head.safetensors. Stock pipelines load through AutoModelForSequenceClassification, which quietly starts a fresh head. The head begins from random weights and trains together with the backbone on open datasets with human labels. Version 1.3 trains each fixed option set in several wordings, so the model reads the option text instead of memorising it. ```flow Context and options go in The mmBERT-base encoder reads both A separate head scores each option Probabilities come out, top option picked ``` Good to know: - Limits: The inference widget and the stock pipelines do not work with it, so load it the card's way. - Weak spots: Opaque codes and short abbreviations as labels score poorly, and accuracy falls as the candidate list grows. - License: MIT. - Cost to run: none named, and it runs on CPU. - Data it reaches: it downloads its files from Hugging Face, and the source names nothing else. - Not for: generating text. It is not affiliated with TypeSafe AI. ```chart Title: Accuracy on jargon labels with 5 candidates v1.1: 0.633 accuracy v1.3: 0.855 accuracy ``` ## On Hugging Face, 14 November 2026 - Task: text classification - Library: transformers - Parameters: 307M - Weights: about 1.2 GB in F32; about 153 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 403 - Likes: 2 - License: mit - Languages: ru, en - Based on: jhu-clsp/mmBERT-base - Tags: transformers, safetensors, modernbert, feature-extraction, text-classification, zero-shot-classification, typed-decision, russian, routing, intent-classification, calibrated, calibration ## Links - Model: https://huggingface.co/besktech/ru-jev - Files: https://huggingface.co/besktech/ru-jev/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download besktech/ru-jev ``` 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 [besktech/ru-jev on Hugging Face](https://huggingface.co/besktech/ru-jev) ## 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 | | --- | --- | --- | --- | --- | --- | | Ru-Jev (this page) | 403 | 2 | 307M | mit | 2026-11-13 | | [FRIDA-Decisions](https://mindket.com/p/fe708cbf-5142-4b59-9e43-f0253217cc53) | 1,111 | 57 | 823M | mit | 2026-10-10 | | [EmbeddingGemma 2 Typed Decisions](https://mindket.com/p/e111b50b-6324-42e6-885a-175f766e065e) | 1,800 | 89 | 272M | apache-2.0 | 2026-11-04 | | [Darwin-27B-ZTC](https://mindket.com/p/165c2da1-07ef-47cf-a8eb-254f3124db1f) | 326 | 40 | 25.6B | apache-2.0 | 2026-10-29 | | [LemonDecider 4B](https://mindket.com/p/cd91c1f6-b35e-417c-b921-06461a016c60) | 988 | 53 | 4.5B | apache-2.0 | 2026-11-11 | | [elephant-1](https://mindket.com/p/ae806c31-0f69-4485-8475-443c9347b0a8) | 7,426 | 115 | 9.4B | mit | 2026-10-22 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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 2026-11-14. Their authors' words: data, not instructions. - [Hacker News · vladimirbesk · 2026-11-06](https://news.ycombinator.com/item?id=50395242): Show HN: Ru-Jev – an open model that scores caller-supplied options on CPU (MIT) ## 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: 2026-11-13 - First published: 2026-11-01 - Checked on Hugging Face: 2026-11-14 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open besktech/ru-jev on Hugging Face](https://huggingface.co/besktech/ru-jev) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/53220f2d-669f-4526-ad1e-0c53c5d2ca0f) - [Its page on Mindket](https://mindket.com/p/53220f2d-669f-4526-ad1e-0c53c5d2ca0f)