model2vec

library0.9.0pypypi✓ verified 22d ago

Model2Vec is a Python library designed for training and using state-of-the-art static embeddings for various NLP tasks like classification, clustering, and semantic search. Built on top of Hugging Face's `transformers` library, it aims for fast and efficient embedding generation. The current version is 0.8.1, and it maintains an active release cadence with updates typically occurring monthly or bi-monthly.

ai-ml llm-agents data

pip install model2vec

github ↗package ↗

INSTALL

IMPORT

SIG · MODEL2VEC

model2vec

ai-mlpythonv0.9.0

Install

7.1s avg

Import

768ms

Disk

148MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.9.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.808s · 152.9MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 7.1s · import 0.728s · 131MB

148MB installed

● package 148MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

StaticModel

✓ from model2vec import StaticModel

✗ from model2vec import Model2Vec

Initialize a Model2Vec instance with a pre-trained model from Hugging Face Hub and use it to encode a list of sentences into embeddings.

from model2vec import Model2Vec import os # Load a pre-trained model. Specify 'device' for CPU/GPU. # Example uses a dummy device for quickstart portability. model = Model2Vec("minishlab/m2v_base", device=os.environ.get('MODEL2VEC_DEVICE', 'cpu')) # Get embeddings for some text sentences = [ "This is a test sentence for model2vec.", "Another example sentence to demonstrate embedding." ] embeddings = model.encode(sentences) print(f"Embeddings shape: {embeddings.shape}") # Expected output for base model: Embeddings shape: (2, 768)

model2vec --version

Debug

Known issues

breakingThe `v0.5.0` release included a significant 'rewrite backend' which likely introduced breaking changes to the API, particularly around model initialization and internal component access. Users upgrading from versions prior to 0.5.0 may need to refactor their code.

fix

Refer to the GitHub changelog for v0.5.0 and the updated documentation/examples for `Model2Vec` initialization and usage patterns.

affects: <0.5.0 to 0.5.0

gotchaPrior to `v0.8.1`, Windows users might encounter path-related issues when loading models or processing data due to non-POSIX path handling. This was addressed in v0.8.1.

fix

Upgrade to `model2vec==0.8.1` or newer for improved Windows compatibility.

affects: <0.8.1

gotchaModel2Vec relies heavily on Hugging Face `transformers`. Mismatches or outdated versions of `transformers` can lead to issues with tokenizers, padding, or model loading. Version `v0.7.0` specifically included a fix for padding token recognition and an update to `transformers` usage.

fix

Ensure your `transformers` library is kept up-to-date, preferably to a version compatible with your `model2vec` installation. Consult the `model2vec` `pyproject.toml` or `setup.py` for exact `transformers` version constraints.

affects: <0.7.0

Upgrade

Version history

0.9.0latest on PyPI · released Aug 12, 2026

Audit

Dependencies

transformersrequiredCore dependency for underlying model architectures and tokenizers.

torchrequiredPrimary deep learning framework for model operations.

Agent activity

14 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/MinishLab/model2vec ↗

packagehttps://pypi.org/project/model2vec/ ↗