The fundamental package for scientific computing with Python.

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</h1><br>


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NumPy is the fundamental package for scientific computing with Python.

- **Website:** https://www.numpy.org
- **Documentation:** https://numpy.org/doc
- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion
- **Source code:** https://github.com/numpy/numpy
- **Contributing:** https://www.numpy.org/devdocs/dev/index.html
- **Bug reports:** https://github.com/numpy/numpy/issues
- **Report a security vulnerability:** https://tidelift.com/docs/security

It provides:

- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities

Testing:

NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:

    python -c 'import numpy; numpy.test()'

Code of Conduct
----------------------

NumPy is a community-driven open source project developed by a diverse group of
[contributors](https://numpy.org/teams/). The NumPy leadership has made a strong
commitment to creating an open, inclusive, and positive community. Please read the
[NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact
with others in a way that makes our community thrive.

Call for Contributions
----------------------

The NumPy project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions
to the source code, please contact us through the [mailing
list](https://mail.python.org/mailman/listinfo/numpy-discussion) first.

Writing code isn’t the only way to contribute to NumPy. You can also:
- review pull requests
- help us stay on top of new and old issues
- develop tutorials, presentations, and other educational materials
- maintain and improve [our website](https://github.com/numpy/numpy.org)
- develop graphic design for our brand assets and promotional materials
- translate website content
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts

For more information about the ways you can contribute to NumPy, visit [our website](https://numpy.org/contribute/). 
If you’re unsure where to start or how your skills fit in, reach out! You can
ask on the mailing list or here, on GitHub, by opening a new issue or leaving a
comment on a relevant issue that is already open.

Our preferred channels of communication are all public, but if you’d like to
speak to us in private first, contact our community coordinators at
numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for
an invitation).

We also have a biweekly community call, details of which are announced on the
mailing list. You are very welcome to join.

If you are new to contributing to open source, [this
guide](https://opensource.guide/how-to-contribute/) helps explain why, what,
and how to successfully get involved.




numpy

.. image:: https://github.com/scipy/scipy/blob/main/doc/source/_static/logo.svg
  :target: https://scipy.org
  :width: 110
  :height: 110
  :align: left 

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  :target: https://anaconda.org/conda-forge/scipy

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.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue
  :target: https://www.nature.com/articles/s41592-019-0686-2

SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
science, and engineering. It includes modules for statistics, optimization,
integration, linear algebra, Fourier transforms, signal and image processing,
ODE solvers, and more.

- **Website:** https://scipy.org
- **Documentation:** https://docs.scipy.org/doc/scipy/
- **Development version of the documentation:** https://scipy.github.io/devdocs
- **Mailing list:** https://mail.python.org/mailman3/lists/scipy-dev.python.org/
- **Source code:** https://github.com/scipy/scipy
- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
- **Bug reports:** https://github.com/scipy/scipy/issues
- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
- **Report a security vulnerability:** https://tidelift.com/docs/security
- **Citing in your work:** https://www.scipy.org/citing-scipy/

SciPy is built to work with
NumPy arrays, and provides many user-friendly and efficient numerical routines,
such as routines for numerical integration and optimization. Together, they
run on all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be depended
upon by some of the world's leading scientists and engineers. If you need to
manipulate numbers on a computer and display or publish the results, give
SciPy a try!

For the installation instructions, see `our install
guide <https://docs.scipy.org/doc/scipy/getting_started.html#installation>`__.


Call for Contributions
----------------------

We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
first issue" may be a good starting point. Have a look at `our contributing
guide <https://scipy.github.io/devdocs/dev/index.html>`__.

Writing code isn’t the only way to contribute to SciPy. You can also:

- review pull requests
- triage issues
- develop tutorials, presentations, and other educational materials
- maintain and improve `our website <https://github.com/scipy/scipy.org>`__
- develop graphic design for our brand assets and promotional materials
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts

If you’re unsure where to start or how your skills fit in, reach out! You can
ask on the mailing list or here, on GitHub, by leaving a
comment on a relevant issue that is already open.

If you are new to contributing to open source, `this
guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
and how to get involved.


scipy

<img src="https://raw.githubusercontent.com/mwaskom/seaborn/master/doc/_static/logo-wide-lightbg.svg"><br>

--------------------------------------

seaborn: statistical data visualization
=======================================

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Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.


Documentation
-------------

Online documentation is available at [seaborn.pydata.org](https://seaborn.pydata.org).

The docs include a [tutorial](https://seaborn.pydata.org/tutorial.html), [example gallery](https://seaborn.pydata.org/examples/index.html), [API reference](https://seaborn.pydata.org/api.html), [FAQ](https://seaborn.pydata.org/faq), and other useful information.

To build the documentation locally, please refer to [`doc/README.md`](doc/README.md).

Dependencies
------------

Seaborn supports Python 3.7+ and no longer supports Python 2.

Installation requires [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and [matplotlib](https://matplotlib.org/). Some advanced statistical functionality requires [scipy](https://www.scipy.org/) and/or [statsmodels](https://www.statsmodels.org/).


Installation
------------

The latest stable release (and required dependencies) can be installed from PyPI:

    pip install seaborn

It is also possible to include optional statistical dependencies (only relevant for v0.12+):

    pip install seaborn[stats]

Seaborn can also be installed with conda:

    conda install seaborn

Note that the main anaconda repository lags PyPI in adding new releases, but conda-forge (`-c conda-forge`) typically updates quickly.

Citing
------

A paper describing seaborn has been published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.03021). The paper provides an introduction to the key features of the library, and it can be used as a citation if seaborn proves integral to a scientific publication.

Testing
-------

Testing seaborn requires installing additional dependencies; they can be installed with the `dev` extra (e.g., `pip install .[dev]`).

To test the code, run `make test` in the source directory. This will exercise the unit tests (using [pytest](https://docs.pytest.org/)) and generate a coverage report.

Code style is enforced with `flake8` using the settings in the [`setup.cfg`](./setup.cfg) file. Run `make lint` to check. Alternately, you can use `pre-commit` to automatically run lint checks on any files you are committing: just run `pre-commit install` to set it up, and then commit as usual going forward.

Development
-----------

Seaborn development takes place on Github: https://github.com/mwaskom/seaborn

Please submit bugs that you encounter to the [issue tracker](https://github.com/mwaskom/seaborn/issues) with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is a [seaborn tag](https://stackoverflow.com/questions/tagged/seaborn).


seaborn

[![PyPi](https://badge.fury.io/py/matplotlib.svg)](https://badge.fury.io/py/matplotlib)
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![image](https://matplotlib.org/_static/logo2.svg)

Matplotlib is a comprehensive library for creating static, animated, and
interactive visualizations in Python.

Check out our [home page](https://matplotlib.org/) for more information.

![image](https://matplotlib.org/_static/readme_preview.png)

Matplotlib produces publication-quality figures in a variety of hardcopy
formats and interactive environments across platforms. Matplotlib can be
used in Python scripts, Python/IPython shells, web application servers,
and various graphical user interface toolkits.

## Install

See the [install
documentation](https://matplotlib.org/stable/users/installing/index.html),
which is generated from `/doc/users/installing/index.rst`

## Contribute

You've discovered a bug or something else you want to change -
excellent!

You've worked out a way to fix it -- even better!

You want to tell us about it -- best of all!

Start at the [contributing
guide](https://matplotlib.org/devdocs/devel/contributing.html)!

## Contact

[Discourse](https://discourse.matplotlib.org/) is the discussion forum
for general questions and discussions and our recommended starting
point.

Our active mailing lists (which are mirrored on Discourse) are:

-   [Users](https://mail.python.org/mailman/listinfo/matplotlib-users)
    mailing list: <matplotlib-users@python.org>
-   [Announcement](https://mail.python.org/mailman/listinfo/matplotlib-announce)
    mailing list: <matplotlib-announce@python.org>
-   [Development](https://mail.python.org/mailman/listinfo/matplotlib-devel)
    mailing list: <matplotlib-devel@python.org>

[Gitter](https://gitter.im/matplotlib/matplotlib) is for coordinating
development and asking questions directly related to contributing to
matplotlib.

## Citing Matplotlib

If Matplotlib contributes to a project that leads to publication, please
acknowledge this by citing Matplotlib.

[A ready-made citation
entry](https://matplotlib.org/stable/users/project/citing.html) is
available.


matplotlib


==============================================
gensim -- Topic Modelling in Python
==============================================

|GA|_
|Wheel|_

.. |GA| image:: https://github.com/RaRe-Technologies/gensim/actions/workflows/tests.yml/badge.svg?branch=develop
.. |Wheel| image:: https://img.shields.io/pypi/wheel/gensim.svg

.. _GA: https://github.com/RaRe-Technologies/gensim/actions
.. _Downloads: https://pypi.python.org/pypi/gensim
.. _License: http://radimrehurek.com/gensim/about.html
.. _Wheel: https://pypi.python.org/pypi/gensim

Gensim is a Python library for *topic modelling*, *document indexing* and *similarity retrieval* with large corpora.
Target audience is the *natural language processing* (NLP) and *information retrieval* (IR) community.

Features
---------

* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core)
* **Intuitive interfaces**

  * easy to plug in your own input corpus/datastream (simple streaming API)
  * easy to extend with other Vector Space algorithms (simple transformation API)

* Efficient multicore implementations of popular algorithms, such as online **Latent Semantic Analysis (LSA/LSI/SVD)**,
  **Latent Dirichlet Allocation (LDA)**, **Random Projections (RP)**, **Hierarchical Dirichlet Process (HDP)** or **word2vec deep learning**.
* **Distributed computing**: can run *Latent Semantic Analysis* and *Latent Dirichlet Allocation* on a cluster of computers.
* Extensive `documentation and Jupyter Notebook tutorials <https://github.com/RaRe-Technologies/gensim/#documentation>`_.


If this feature list left you scratching your head, you can first read more about the `Vector
Space Model <http://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised
document analysis <http://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia.

Installation
------------

This software depends on `NumPy and Scipy <http://www.scipy.org/Download>`_, two Python packages for scientific computing.
You must have them installed prior to installing `gensim`.

It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as MKL, `ATLAS <http://math-atlas.sourceforge.net/>`_ or `OpenBLAS <http://xianyi.github.io/OpenBLAS/>`_ is known to improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don't need to do anything special.

Install the latest version of gensim::

    pip install --upgrade gensim

Or, if you have instead downloaded and unzipped the `source tar.gz <http://pypi.python.org/pypi/gensim>`_ package::

    python setup.py install


For alternative modes of installation, see the `documentation <http://radimrehurek.com/gensim/#install>`_.

Gensim is being `continuously tested <http://radimrehurek.com/gensim/#testing>`_ under all `supported Python versions <https://github.com/RaRe-Technologies/gensim/wiki/Gensim-And-Compatibility>`_.
Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.


How come gensim is so fast and memory efficient? Isn't it pure Python, and isn't Python slow and greedy?
--------------------------------------------------------------------------------------------------------

Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured).

Memory-wise, gensim makes heavy use of Python's built-in generators and iterators for streamed data processing. Memory efficiency was one of gensim's `design goals <http://radimrehurek.com/gensim/about.html>`_, and is a central feature of gensim, rather than something bolted on as an afterthought.

Documentation
-------------
* `QuickStart`_
* `Tutorials`_
* `Tutorial Videos`_
* `Official Documentation and Walkthrough`_

Citing gensim
-------------

When `citing gensim in academic papers and theses <https://scholar.google.cz/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:u-x6o8ySG0sC>`_, please use this BibTeX entry::

  @inproceedings{rehurek_lrec,
        title = {{Software Framework for Topic Modelling with Large Corpora}},
        author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka},
        booktitle = {{Proceedings of the LREC 2010 Workshop on New
             Challenges for NLP Frameworks}},
        pages = {45--50},
        year = 2010,
        month = May,
        day = 22,
        publisher = {ELRA},
        address = {Valletta, Malta},
        language={English}
  }

----------------

Gensim is open source software released under the `GNU LGPLv2.1 license <http://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html>`_.
Copyright (c) 2009-now Radim Rehurek

|Analytics|_

.. |Analytics| image:: https://ga-beacon.appspot.com/UA-24066335-5/your-repo/page-name
.. _Analytics: https://github.com/igrigorik/ga-beacon
.. _Official Documentation and Walkthrough: http://radimrehurek.com/gensim/
.. _Tutorials: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials
.. _Tutorial Videos: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#videos
.. _QuickStart: https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/core/run_core_concepts.html



gensim

Multilingual Sentence & Image Embeddings with BERT

[![GitHub - License](https://img.shields.io/github/license/UKPLab/sentence-transformers?logo=github&style=flat&color=green)][#github-license]
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sentence-transformers?logo=pypi&style=flat&color=blue)][#pypi-package]
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[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/sentence-transformers?logo=anaconda&style=flat&color=orange)][#conda-forge-package]
[![Docs - GitHub.io](https://img.shields.io/static/v1?logo=github&style=flat&color=pink&label=docs&message=sentence-transformers)][#docs-package]


[#github-license]: https://github.com/UKPLab/sentence-transformers/blob/master/LICENSE
[#pypi-package]: https://pypi.org/project/sentence-transformers/
[#conda-forge-package]: https://anaconda.org/conda-forge/sentence-transformers
[#docs-package]: https://www.sbert.net/


# Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT & Co.

This framework provides an easy method to compute dense vector representations for **sentences**, **paragraphs**, and **images**. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity.

We provide an increasing number of **[state-of-the-art pretrained models](https://www.sbert.net/docs/pretrained_models.html)** for more than 100 languages, fine-tuned for various use-cases.

Further, this framework allows an easy  **[fine-tuning of custom embeddings models](https://www.sbert.net/docs/training/overview.html)**, to achieve maximal performance on your specific task.

For the **full documentation**, see **[www.SBERT.net](https://www.sbert.net)**.

The following publications are integrated in this framework:

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) (EMNLP 2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813) (EMNLP 2020)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://arxiv.org/abs/2010.08240) (NAACL 2021)
- [The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes](https://arxiv.org/abs/2012.14210) (arXiv 2020)
- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979) (arXiv 2021)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://arxiv.org/abs/2104.08663) (arXiv 2021)

## Installation

We recommend **Python 3.6** or higher, **[PyTorch 1.6.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.6.0](https://github.com/huggingface/transformers)** or higher. The code does **not** work with Python 2.7.

**Install with pip**

Install the *sentence-transformers* with `pip`:

```
pip install -U sentence-transformers
```

**Install with conda**

You can install the *sentence-transformers* with `conda`:

```
conda install -c conda-forge sentence-transformers
```

**Install from sources**

Alternatively, you can also clone the latest version from the [repository](https://github.com/UKPLab/sentence-transformers) and install it directly from the source code:

````
pip install -e .
```` 

**PyTorch with CUDA**

If you want to use a GPU / CUDA, you must install PyTorch with the matching CUDA Version. Follow
[PyTorch - Get Started](https://pytorch.org/get-started/locally/) for further details how to install PyTorch.

## Getting Started

See [Quickstart](https://www.sbert.net/docs/quickstart.html) in our documenation.

[This example](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications/computing-embeddings/computing_embeddings.py) shows you how to use an already trained Sentence Transformer model to embed sentences for another task.

First download a pretrained model.

````python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
````

Then provide some sentences to the model.

````python
sentences = ['This framework generates embeddings for each input sentence',
    'Sentences are passed as a list of string.', 
    'The quick brown fox jumps over the lazy dog.']
sentence_embeddings = model.encode(sentences)
````

And that's it already. We now have a list of numpy arrays with the embeddings.

````python
for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")
````

## Pre-Trained Models

We provide a large list of [Pretrained Models](https://www.sbert.net/docs/pretrained_models.html) for more than 100 languages. Some models are general purpose models, while others produce embeddings for specific use cases. Pre-trained models can be loaded by just passing the model name: `SentenceTransformer('model_name')`.

[»  Full list of pretrained models](https://www.sbert.net/docs/pretrained_models.html)

## Training

This framework allows you to fine-tune your own sentence embedding methods, so that you get task-specific sentence embeddings. You have various options to choose from in order to get perfect sentence embeddings for your specific task. 

See [Training Overview](https://www.sbert.net/docs/training/overview.html) for an introduction how to train your own embedding models. We provide [various examples](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) how to train models on various datasets.

Some highlights are:
- Support of various transformer networks including BERT, RoBERTa, XLM-R, DistilBERT, Electra, BART, ...
- Multi-Lingual and multi-task learning
- Evaluation during training to find optimal model
- [10+ loss-functions](https://www.sbert.net/docs/package_reference/losses.html) allowing to tune models specifically for semantic search, paraphrase mining, semantic similarity comparison, clustering, triplet loss, contrastive loss.

## Performance

Our models are evaluated extensively on 15+ datasets including challening domains like Tweets, Reddit, emails. They achieve by far the **best performance** from all available sentence embedding methods. Further, we provide several **smaller models** that are **optimized for speed**.

[» Full list of pretrained models](https://www.sbert.net/docs/pretrained_models.html)

## Application Examples

You can use this framework for:

- [Computing Sentence Embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html)
- [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html)
- [Clustering](https://www.sbert.net/examples/applications/clustering/README.html)
- [Paraphrase Mining](https://www.sbert.net/examples/applications/paraphrase-mining/README.html)
 - [Translated Sentence Mining](https://www.sbert.net/examples/applications/parallel-sentence-mining/README.html)
 - [Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
 - [Retrieve & Re-Rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) 
 - [Text Summarization](https://www.sbert.net/examples/applications/text-summarization/README.html) 
- [Multilingual Image Search, Clustering & Duplicate Detection](https://www.sbert.net/examples/applications/image-search/README.html)

and many more use-cases.

For all examples, see [examples/applications](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications).

## Citing & Authors

If you find this repository helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):

```bibtex 
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

If you use one of the multilingual models, feel free to cite our publication [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813):

```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
```

Please have a look at [Publications](https://www.sbert.net/docs/publications.html) for our different publications that are integrated into SentenceTransformers.

Contact person: [Nils Reimers](https://www.nils-reimers.de), [info@nils-reimers.de](mailto:info@nils-reimers.de)

https://www.ukp.tu-darmstadt.de/

Don't hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn't be) or if you have further questions.

> This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

sentence-transformers

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