<commit_before>SentenceTransformers Documentation
=================================================

SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper `Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks <https://arxiv.org/abs/1908.10084>`_.

You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for `semantic textual similar <docs/usage/semantic_textual_similarity.html>`_, `semantic search <examples/applications/semantic-search/README.html>`_, or `paraphrase mining <examples/applications/paraphrase-mining/README.html>`_.

The framework is based on `PyTorch <https://pytorch.org/>`_ and `Transformers <https://huggingface.co/transformers/>`_ and offers a large collection of `pre-trained models <docs/pretrained_models.html>`_ tuned for various tasks. Further, it is easy to `fine-tune your own models <docs/training/overview.html>`_.


Installation
=================================================

You can install it using pip:

.. code-block:: python

   pip install -U sentence-transformers


We recommand **Python 3.6** or higher, and at least **PyTorch 1.6.0**. See `installation <docs/installation.html>`_ for further installation options, especially if you want to use a GPU.



Usage
=================================================
The usage is as simple as:
<commit_msg>Spelling<commit_after>SentenceTransformers Documentation
=================================================

SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper `Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks <https://arxiv.org/abs/1908.10084>`_.

You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for `semantic textual similar <docs/usage/semantic_textual_similarity.html>`_, `semantic search <examples/applications/semantic-search/README.html>`_, or `paraphrase mining <examples/applications/paraphrase-mining/README.html>`_.

The framework is based on `PyTorch <https://pytorch.org/>`_ and `Transformers <https://huggingface.co/transformers/>`_ and offers a large collection of `pre-trained models <docs/pretrained_models.html>`_ tuned for various tasks. Further, it is easy to `fine-tune your own models <docs/training/overview.html>`_.


Installation
=================================================

You can install it using pip:

.. code-block:: python

   pip install -U sentence-transformers


We recommend **Python 3.6** or higher, and at least **PyTorch 1.6.0**. See `installation <docs/installation.html>`_ for further installation options, especially if you want to use a GPU.



Usage
=================================================
The usage is as simple as:

{'fraction_non_alphanumeric': 0.17399678972712682, 'fraction_numerical': 0.010914927768860353, 'mean_word_length': 5.505219206680585, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 6, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3035630', 'n_tokens_mistral': 885, 'n_tokens_neox': 796, 'n_words': 310}