--- title: "UIBK-Embedding-v1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1bccd540-6386-41f9-973b-5d7581f35096" updated: "2026-11-13" --- # UIBK-Embedding-v1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds the passages that best match a query, using one model that combines token level and whole text matching. An English retrieval model from the University of Innsbruck for people building search. UIBK-Embedding-v1 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 DataScience-UIBK ## About ### What it is This is a retrieval model for English text. The data science group at the University of Innsbruck trained it starting from ModernBERT-base. Search systems usually choose between a single vector per text and many token vectors per text. This model produces both from one encoder pass and keeps them in a single index, so you run one model instead of two. ### Who it suits Developers and researchers who build search or retrieval over English text, and who already use or want to try PyLate and late interaction retrieval. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Search engineering | Developers who build document search | Index documents with the model and rank them against queries | Searching a set of support articles with one index instead of two | | Research | Information retrieval researchers | Compare it with other retrievers on benchmarks such as BEIR | Checking its average score against another ColBERT model | | Knowledge tools | Teams that build question answering systems | Use it as the retrieval step before an answer is written | Fetching the passages a question refers to | Skip it if your text is not in English, or if you need a model that writes answers, since this one only ranks text. ### Where to use it The model loads as a PyLate ColBERT model and works with PLAID indexes as they are. The files download on Windows, macOS or Linux. - PyLate: the library the model is made for - PLAID indexes: the indexing method it works with - MTEB: the benchmark suite the authors used to measure it - Windows, macOS or Linux: where you can fetch the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Document retrieval | Search developers | A ranked list of matching passages | | Benchmark comparison | Researchers | Scores to compare with other retrievers | | Retrieval for question answering | Application teams | Candidate passages for a later step | ### How to set it up You need Python and the PyLate library. The model files come from Hugging Face. - Python with pip - The PyLate library - A small amount of disk space, about 596 MB for the F32 weights 1. Install the Hugging Face hub library. 2. Download the model files. 3. Load them as a PyLate ColBERT model and build your index. ```sh pip install -U huggingface_hub hf download DataScience-UIBK/uibk-embedding-v1 ``` ### How to use it Load the model in PyLate as you would any ColBERT model, then index your documents and score queries against them. Documents can run to 2048 tokens and queries to 256 tokens. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | PyLate | Loads the model and scores queries against documents | | PLAID | Indexes the vectors for fast search | ### Example You index a set of English articles with the model, then search with a question. The ranking comes back from a plain token matching score over one list of vectors, which already includes the whole text score. ### For newcomers and experts If you are new to this, think of it as a way to rank passages by how well they answer a question. Start by indexing a few dozen short English documents and try one query. If you know the field, each text becomes a set of vectors with 128 dimensions, as in any ColBERT model. Three extra vectors in that list carry the dense embedding, so plain MaxSim equals the token score plus a weighted dense score. The card says it was trained with knowledge distillation from a public cross encoder. One encoder pass reads the text → Token vectors and dense vectors form one list → One PLAID index stores the list → Plain MaxSim ranks the results ### Good to know The authors report their own numbers. BEIR average nDCG@10, 15 datasets | Model | Average | | --- | --- | | UIBK-Embedding-v1 | 59.79 | | LateOn | 57.22 | The card adds that this model is larger and reads longer documents than LateOn. LateOn climbs to 57.69 when the authors give it documents of 2048 tokens. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the documents and queries you pass in - Not for: non-English text, since the card lists English only ## README ````markdown # UIBK-Embedding-v1 Finds the passages that best match a query, using one model that combines token level and whole text matching. An English retrieval model from the University of Innsbruck for people building search. Made by DataScience-UIBK 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: This is a retrieval model for English text. The data science group at the University of Innsbruck trained it starting from ModernBERT-base. Search systems usually choose between a single vector per text and many token vectors per text. This model produces both from one encoder pass and keeps them in a single index, so you run one model instead of two. Who it suits: Developers and researchers who build search or retrieval over English text, and who already use or want to try PyLate and late interaction retrieval. ```table Industry | Who uses it | How they use it | Example Search engineering | Developers who build document search | Index documents with the model and rank them against queries | Searching a set of support articles with one index instead of two Research | Information retrieval researchers | Compare it with other retrievers on benchmarks such as BEIR | Checking its average score against another ColBERT model Knowledge tools | Teams that build question answering systems | Use it as the retrieval step before an answer is written | Fetching the passages a question refers to ``` Skip it if your text is not in English, or if you need a model that writes answers, since this one only ranks text. Where to use it: The model loads as a PyLate ColBERT model and works with PLAID indexes as they are. The files download on Windows, macOS or Linux. - PyLate: the library the model is made for - PLAID indexes: the indexing method it works with - MTEB: the benchmark suite the authors used to measure it - Windows, macOS or Linux: where you can fetch the files with Python ```table Use case | Who | What they get back Document retrieval | Search developers | A ranked list of matching passages Benchmark comparison | Researchers | Scores to compare with other retrievers Retrieval for question answering | Application teams | Candidate passages for a later step ``` How to set it up: You need Python and the PyLate library. The model files come from Hugging Face. - Python with pip - The PyLate library - A small amount of disk space, about 596 MB for the F32 weights 1. Install the Hugging Face hub library. 2. Download the model files. 3. Load them as a PyLate ColBERT model and build your index. ```sh pip install -U huggingface_hub hf download DataScience-UIBK/uibk-embedding-v1 ``` How to use it: Load the model in PyLate as you would any ColBERT model, then index your documents and score queries against them. Documents can run to 2048 tokens and queries to 256 tokens. ```table Tool | What it does hf download | Fetches the model files to your computer PyLate | Loads the model and scores queries against documents PLAID | Indexes the vectors for fast search ``` Example: You index a set of English articles with the model, then search with a question. The ranking comes back from a plain token matching score over one list of vectors, which already includes the whole text score. For newcomers and experts: If you are new to this, think of it as a way to rank passages by how well they answer a question. Start by indexing a few dozen short English documents and try one query. If you know the field, each text becomes a set of vectors with 128 dimensions, as in any ColBERT model. Three extra vectors in that list carry the dense embedding, so plain MaxSim equals the token score plus a weighted dense score. The card says it was trained with knowledge distillation from a public cross encoder. ```flow One encoder pass reads the text Token vectors and dense vectors form one list One PLAID index stores the list Plain MaxSim ranks the results ``` Good to know: The authors report their own numbers. ```table Title: BEIR average nDCG@10, 15 datasets Model | Average UIBK-Embedding-v1 | 59.79 LateOn | 57.22 ``` The card adds that this model is larger and reads longer documents than LateOn. LateOn climbs to 57.69 when the authors give it documents of 2048 tokens. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the documents and queries you pass in - Not for: non-English text, since the card lists English only ## On Hugging Face, 13 November 2026 - Task: sentence similarity - Library: PyLate - Parameters: 149M - Weights: about 596 MB in F32; about 75 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,612 - Likes: 11 - License: apache-2.0 - Languages: en - Based on: answerdotai/ModernBERT-base - Fine-tunes: 1 - Tags: pylate, safetensors, modernbert, colbert, multi-vector, sentence-transformers, sentence-similarity, feature-extraction, late-interaction, text-embeddings-inference ## Links - Model: https://huggingface.co/DataScience-UIBK/uibk-embedding-v1 - Files: https://huggingface.co/DataScience-UIBK/uibk-embedding-v1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download DataScience-UIBK/uibk-embedding-v1 ``` 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 [DataScience-UIBK/uibk-embedding-v1 on Hugging Face](https://huggingface.co/DataScience-UIBK/uibk-embedding-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 | | --- | --- | --- | --- | --- | --- | | UIBK-Embedding-v1 (this page) | 2,612 | 11 | 149M | apache-2.0 | 2026-10-10 | | [hs-embed-1](https://mindket.com/p/68203fd5-48b7-49fa-ba02-80ddf5ece256) | 22,412 | 7 | 112M | mit | 2026-11-02 | | [SPARSEUP](https://mindket.com/p/3b9a9c3d-95d0-484b-bf5c-3c0a23747733) | 6,627 | 63 | 188M | apache-2.0 | 2026-09-22 | | [KURE-Sparse-nano](https://mindket.com/p/36a18f34-06fc-4e32-881b-24ab59032524) | 973 | 19 | 149M | apache-2.0 | 2026-11-01 | | [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 | 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: 2026-10-10 - First published: 2026-10-09 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open DataScience-UIBK/uibk-embedding-v1 on Hugging Face](https://huggingface.co/DataScience-UIBK/uibk-embedding-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1bccd540-6386-41f9-973b-5d7581f35096) - [Its page on Mindket](https://mindket.com/p/1bccd540-6386-41f9-973b-5d7581f35096)