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# OpenVINO Tokenizers: Incorporate Text Processing Into OpenVINO Pipelines | |
[](https://mybinder.org/v2/gh/eaidova/openvino_notebooks_binder.git/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fopenvinotoolkit%252Fopenvino_notebooks%26urlpath%3Dtree%252Fopenvino_notebooks%252Fnotebooks%2Fopenvino-tokenizers%2Fopenvino-tokenizers.ipynb) | |
[](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/openvino-tokenizers/openvino-tokenizers.ipynb) | |
<center><img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/51917466/047f9167-a4ef-4d3d-a33b-d124541f9e2c"></center> | |
OpenVINO Tokenizers is an OpenVINO extension and a Python library designed to streamline tokenizer conversion for seamless integration into your projects. It supports Python and C++ environments and is compatible with all major platforms: Linux, Windows, and MacOS. | |
## Notebook Contents | |
The tutorial consists of the following steps: | |
- Explain the basics of tokenization | |
- Install OpenVINO Tokenizers | |
- Convert tokenizer from HuggingFace Hub using CLI and Python API | |
- Create a Text Generation pipeline with OpenVINO tokenizer and detokenizer | |
- Combine an OpenVINO tokenizer with a classification model | |
## Installation Instructions | |
This is a self-contained example that relies solely on its own code.</br> | |
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. | |
For details, please refer to [Installation Guide](../../README.md). | |