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v0.9 (#254)
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README.md

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[![PyPI - Python](https://img.shields.io/badge/python-3.6%20|%203.7%20|%203.8-blue.svg)](https://pypi.org/project/keybert/)
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[![PyPI - Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://pypi.org/project/keybert/)
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[![PyPI - License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/MaartenGr/keybert/blob/master/LICENSE)
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[![PyPI - PyPi](https://img.shields.io/pypi/v/keyBERT)](https://pypi.org/project/keybert/)
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[![Build](https://img.shields.io/github/actions/workflow/status/MaartenGr/keyBERT/testing.yml?branch=master)](https://pypi.org/keybert/)

docs/changelog.md

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## **Version 0.9.0**
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*Release date: 05 Februari, 2025*
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**Model2Vec**
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You can use Model2Vec for blazingly fast embedding as follows:
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```python
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from keybert import KeyBERT
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from model2vec import StaticModel
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embedding_model = StaticModel.from_pretrained("minishlab/potion-base-8M")
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kw_model = KeyBERT(embedding_model)
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```
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**Light-weight KeyBERT**
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You can now install a light-weight KeyBERT with:
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`pip install keybert --no-deps scikit-learn model2vec`
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**Fixes**
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* Add Model2Vec & light-weight installation in [#253](https://github.com/MaartenGr/KeyBERT/pull/253)
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* Add Text Generation Inference with JSON output by [@joaomsimoes](https://github.com/joaomsimoes) in [#235](https://github.com/MaartenGr/KeyBERT/pull/235)
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* Update pre-commit hooks [@afuetterer](https://github.com/afuetterer) in [#237](https://github.com/MaartenGr/KeyBERT/pull/237)
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* Set up lint job using pre-commit/action [@afuetterer](https://github.com/afuetterer) in [#238](https://github.com/MaartenGr/KeyBERT/pull/238)
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## **Version 0.8.5**
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*Release date: 14 June, 2024*

pyproject.toml

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[project]
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name = "keybert"
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version = "0.8.5"
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version = "0.9.0"
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description = "KeyBERT performs keyword extraction with state-of-the-art transformer models."
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readme = "README.md"
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license = {file = "LICENSE"}

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