--- title: "UltraGliClass v0.1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/86e56ae5-13bb-4852-a0a4-04aae8cc7b82" updated: "2026-11-13" --- # UltraGliClass v0.1 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 154M-parameter zero-shot text classifier that scores every label you pass in one pass, for single-label, multi-label and intent tasks, including long inputs. For developers who need quick text labeling in Python. UltraGliClass v0.1 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 Srimon12 ## About ### What it is UltraGliClass v0.1 labels text against categories you name on the spot, using a compact model of roughly 150M parameters. You pass a text, a list of labels and, if you like, an instruction. It scores every label in one forward pass, so you do not need a training set for your own labels. It uses the GLiClass head on top of the UltraBERT-150M encoder. ### Who it suits Developers and data teams who need to label text by categories they define themselves, including cases with many labels or long inputs. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Teams routing messages | Match a message to one of many intents | Label a banking request among dozens of intents | | Data engineering | Pipeline builders | Tag documents with custom labels | Mark a long record as one of several decisions | | Research | NLP researchers | Compare zero-shot classifiers on long inputs | Test choice and yes-no decisions at 8,192 tokens | You can skip it if you need a model for languages other than English, since the card lists English only. ### Where to use it - Python with the transformers and gliclass packages - Windows, macOS or Linux, wherever Python runs - Text classification steps inside your own pipelines | Use case | Who | What they get back | | --- | --- | --- | | Single-label classification | Developers | One best label for a text | | Multi-label classification | Data teams | A score for every label you listed | | Intent detection with many labels | Support teams | Scores across dozens or hundreds of intents | | Long decisions | Analysts | Choice, yes-no or score calls over long inputs | ### How to set it up You need Python and the two packages at the versions the card used for training and evaluation. Transformers 5.20 and later supports the encoder natively, so no trust\_remote\_code is needed. - Python and pip - transformers 5.20.2 or newer - gliclass 0.1.21 1. Install the two packages. 2. Download the model, or let the library fetch it by name. 3. Load the model and tokenizer in Python. ```bash pip install "transformers>=5.20.2" "gliclass==0.1.21" ``` ```sh pip install -U huggingface_hub hf download Srimon12/ultragliclass-v0.1 ``` ### How to use it Load the model and the tokenizer, then build a zero-shot classification pipeline. The pipeline takes one text, your candidate labels as strings, and an instruction if you want one. ```python import torch from gliclass import GLiClassModel, ZeroShotClassificationPipeline from transformers import AutoTokenizer repo = "Srimon12/ultragliclass-v0.1" model = GLiClassModel.from_pretrained(repo) tokenizer = AutoTokenizer.from_pretrained(repo, add_prefix_space=False) # trained without a prefix space ``` | Tool | What it does | | --- | --- | | GLiClassModel | Loads the classifier weights | | AutoTokenizer | Loads the tokenizer, with add\_prefix\_space set to False | | ZeroShotClassificationPipeline | Scores each label for a text in one pass | ### Example You pass the text "My card was stolen, please stop it" with the labels lost card, balance enquiry and transfer. The pipeline returns a score for each label, and you take the highest one as the intent. ### For newcomers and experts If you are new, think of it as sorting text into boxes you name yourself. Start with two or three short labels and a few example sentences before you try a long list. For experts, training ran in three stages. The last stage used inputs as long as 8,192 tokens and as many as 256 labels. The encoder supports 32,768 positions, but nothing beyond 8,192 tokens was tested. The trade-off is that it trails gliclass-base-v3.0 on a few sets such as sst5 and 20 Newsgroups. Text, labels and optional instruction → UltraBERT encoder with GLiClass head → One forward pass → A score for every label ### Good to know Average macro-F1 on the 14-set zero-shot suite | Label | Value | | --- | --- | | UltraGliClass v0.1 | 0.688 | | gliclass-base-v3.0 | 0.673 | | gliclass-modern-base-v3.0 | 0.558 | - Language: English - Length: trained and tested at 8,192 tokens, and longer inputs are untested - Results: the figures come from the author's own run, so check them on your data - License: Apache 2.0, with a notice on the card that some training rows carry a share-alike license, so read it before commercial use - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, and your text stays in your own Python process - Not for: languages other than English, or inputs past 8,192 tokens without your own testing ## README ````markdown # UltraGliClass v0.1 A 154M-parameter zero-shot text classifier that scores every label you pass in one pass, for single-label, multi-label and intent tasks, including long inputs. For developers who need quick text labeling in Python. Made by Srimon12 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: UltraGliClass v0.1 labels text against categories you name on the spot, using a compact model of roughly 150M parameters. You pass a text, a list of labels and, if you like, an instruction. It scores every label in one forward pass, so you do not need a training set for your own labels. It uses the GLiClass head on top of the UltraBERT-150M encoder. Who it suits: Developers and data teams who need to label text by categories they define themselves, including cases with many labels or long inputs. ```table Industry | Who uses it | How they use it | Example Customer support | Teams routing messages | Match a message to one of many intents | Label a banking request among dozens of intents Data engineering | Pipeline builders | Tag documents with custom labels | Mark a long record as one of several decisions Research | NLP researchers | Compare zero-shot classifiers on long inputs | Test choice and yes-no decisions at 8,192 tokens ``` You can skip it if you need a model for languages other than English, since the card lists English only. Where to use it: - Python with the transformers and gliclass packages - Windows, macOS or Linux, wherever Python runs - Text classification steps inside your own pipelines ```table Use case | Who | What they get back Single-label classification | Developers | One best label for a text Multi-label classification | Data teams | A score for every label you listed Intent detection with many labels | Support teams | Scores across dozens or hundreds of intents Long decisions | Analysts | Choice, yes-no or score calls over long inputs ``` How to set it up: You need Python and the two packages at the versions the card used for training and evaluation. Transformers 5.20 and later supports the encoder natively, so no trust_remote_code is needed. - Python and pip - transformers 5.20.2 or newer - gliclass 0.1.21 1. Install the two packages. 2. Download the model, or let the library fetch it by name. 3. Load the model and tokenizer in Python. ```bash pip install "transformers>=5.20.2" "gliclass==0.1.21" ``` ```sh pip install -U huggingface_hub hf download Srimon12/ultragliclass-v0.1 ``` How to use it: Load the model and the tokenizer, then build a zero-shot classification pipeline. The pipeline takes one text, your candidate labels as strings, and an instruction if you want one. ```python import torch from gliclass import GLiClassModel, ZeroShotClassificationPipeline from transformers import AutoTokenizer repo = "Srimon12/ultragliclass-v0.1" model = GLiClassModel.from_pretrained(repo) tokenizer = AutoTokenizer.from_pretrained(repo, add_prefix_space=False) # trained without a prefix space ``` ```table Tool | What it does GLiClassModel | Loads the classifier weights AutoTokenizer | Loads the tokenizer, with add_prefix_space set to False ZeroShotClassificationPipeline | Scores each label for a text in one pass ``` Example: You pass the text "My card was stolen, please stop it" with the labels lost card, balance enquiry and transfer. The pipeline returns a score for each label, and you take the highest one as the intent. For newcomers and experts: If you are new, think of it as sorting text into boxes you name yourself. Start with two or three short labels and a few example sentences before you try a long list. For experts, training ran in three stages. The last stage used inputs as long as 8,192 tokens and as many as 256 labels. The encoder supports 32,768 positions, but nothing beyond 8,192 tokens was tested. The trade-off is that it trails gliclass-base-v3.0 on a few sets such as sst5 and 20 Newsgroups. ```flow Text, labels and optional instruction UltraBERT encoder with GLiClass head One forward pass A score for every label ``` Good to know: ```chart Title: Average macro-F1 on the 14-set zero-shot suite UltraGliClass v0.1: 0.688 gliclass-base-v3.0: 0.673 gliclass-modern-base-v3.0: 0.558 ``` - Language: English - Length: trained and tested at 8,192 tokens, and longer inputs are untested - Results: the figures come from the author's own run, so check them on your data - License: Apache 2.0, with a notice on the card that some training rows carry a share-alike license, so read it before commercial use - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, and your text stays in your own Python process - Not for: languages other than English, or inputs past 8,192 tokens without your own testing ## On Hugging Face, 13 November 2026 - Task: zero shot classification - Library: gliclass - Parameters: 154M - Weights: about 615 MB in F32; about 77 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 59 - Likes: 4 - License: apache-2.0 - Languages: en - Based on: dmis-lab/UltraBERT-150M - Tags: gliclass, safetensors, zero-shot-classification, text-classification, long-context, decision-model, ultrabert ## Links - Model: https://huggingface.co/Srimon12/ultragliclass-v0.1 - Files: https://huggingface.co/Srimon12/ultragliclass-v0.1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Srimon12/ultragliclass-v0.1 ``` 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 [Srimon12/ultragliclass-v0.1 on Hugging Face](https://huggingface.co/Srimon12/ultragliclass-v0.1) ## 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 | | --- | --- | --- | --- | --- | --- | | UltraGliClass v0.1 (this page) | 59 | 4 | 154M | apache-2.0 | 2026-11-02 | | [jevos-v4](https://mindket.com/p/cb6031a9-f6ec-4eb2-b36a-0543673bb7d9) | 1,475 | 22 | 882M | mit | 2026-10-09 | | [UltraBERT-380M](https://mindket.com/p/06921117-c5e0-4785-9a69-868cb9995b2d) | 15,830 | 82 | 430M | apache-2.0 | 2026-10-16 | | [Ru-Jev](https://mindket.com/p/53220f2d-669f-4526-ad1e-0c53c5d2ca0f) | 403 | 2 | 307M | mit | 2026-11-13 | | [d3-flash](https://mindket.com/p/9f4e35f9-58d2-4c23-bfde-20d3530ef7ce) | 103,577 | 114 | 8.4B | apache-2.0 | 2026-11-02 | | [d3-mini](https://mindket.com/p/4e08c56c-92f6-4e07-8884-2cd16598fc8b) | 53,115 | 75 | 4.5B | apache-2.0 | 2026-10-30 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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: 2026-11-02 - First published: 2026-11-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Srimon12/ultragliclass-v0.1 on Hugging Face](https://huggingface.co/Srimon12/ultragliclass-v0.1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/86e56ae5-13bb-4852-a0a4-04aae8cc7b82) - [Its page on Mindket](https://mindket.com/p/86e56ae5-13bb-4852-a0a4-04aae8cc7b82)