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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning
| Date | Stars |
|---|---|
| 2026-07-31 | 349 |
| 2026-08-04 | 349 |
| 2026-08-06 | 349 |
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<div align="center"> <img src="https://raw.githubusercontent.com/GRAAL-Research/deepparse/main/docs/source/_static/logos/deepparse.png" width="220" height="91"/> [](https://pypi.org/project/deepparse) [](https://badge.fury.io/py/deepparse) [](https://pepy.tech/project/deepparse) [](https://pepy.tech/project/deepparse) [](https://github.com/GRAAL-Research/deepparse/actions/workflows/formatting.yml) [](https://github.com/GRAAL-Research/deepparse/actions/workflows/linting.yml) [](https://github.com/GRAAL-Research/deepparse/actions/workflows/tests.yml) [](https://github.com/GRAAL-Research/deepparse/actions/workflows/docs.yml) [](https://codecov.io/gh/GRAAL-Research/deepparse) [](https://www.codacy.com/gh/GRAAL-Research/deepparse/dashboard?utm_source=github.com&utm_medium=referral&utm_content=GRAAL-Research/deepparse&utm_campaign=Badge_Grade) <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a> [](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?) [](http://www.gnu.org/licenses/lgpl-3.0) [](https://doi.org/10.5281/zenodo.7010740) [](https://huggingface.co/deepparse) [](https://github.com/GRAAL-Research/deepparse-address-data) </div> ## Here is Deepparse. Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning. Use deepparse to - parse multinational address using one of our pretrained models with or without attention mechanism, - parse addresses directly from the command line without code to write, - parse addresses with our out-of-the-box FastAPI parser, - retrain our pretrained models on new data to improve parsing on specific country address patterns, - retrain our pretrained models with new prediction tags easily, - retrain our pretrained models with or without freezing some layers, - train a new Seq2Seq addresses parsing models easily using a new model configuration. Read the documentation at [deepparse.org](https://deepparse.org). Deepparse is compatible with the __latest version of PyTorch__ and __Python >= 3.10, <= 3.13__. ### Countries and Results We evaluate our models on two forms of address data - **clean data** which refers to addresses containing elements from four categories, namely a street name, a municipality, a province and a postal code, - **incomplete data** which is made up of addresses missing at least one category amongst the aforementioned ones. You can get our dataset [here](https://github.com/GRAAL-Research/deepparse-address-data). #### Clean Data The following table presents the accuracy (using clean data) on the 20 countries we used during training for both our models. Attention mechanisms improve performanc
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matched fp:d6efda05ae9d0383, llm:Repository topics: addresses-parsing, machine-learning, python; description: 'Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning'
matched fp:d6efda05ae9d0383, llm:Repository topics: addresses-parsing, machine-learning, python; description: 'Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning'
matched fp:d6efda05ae9d0383, llm:Repository topics: addresses-parsing, machine-learning, python; description: 'Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning'