Update version according to github

docs/Makefile

 
 docs:
-	sphinx-build -c . -a -E .. _build
+	sphinx-build -c . -a -E .. _build
\ No newline at end of file
+
+docs-quick:
+	sphinx-build -c . .. _build
\ No newline at end of file

docs/_static/css/custom.css

@@ -24,4 +24,88 @@ dl.class > dt {
 
 .wy-side-nav-search {
     padding-top: 0px;
-}
+}
\ No newline at end of file
+
+.components {
+    display: flex;
+    flex-flow: row wrap;
+}
+
+.components > .box {
+    flex: 1;
+    margin: 0.5rem;
+    padding: 1rem;
+    border-style: solid;
+    border-width: 1px;
+    border-radius: 0.5rem;
+    border-color: rgb(55 65 81);
+    background-color: #e3e3e3;
+    color: #404040; /* Override the colors imposed by <a href> */
+}
+
+.components > .box:nth-child(1) > .header {
+    background-image: linear-gradient(to bottom right, #60a5fa, #3b82f6);
+}
+
+.components > .box:nth-child(2) > .header {
+    background-image: linear-gradient(to bottom right, #fb923c, #f97316);
+}
+
+.components > .box:nth-child(3) > .header {
+    background-image: linear-gradient(to bottom right, #f472b6, #ec4899);
+}
+
+.components > .box:nth-child(4) > .header {
+    background-image: linear-gradient(to bottom right, #a78bfa, #8b5cf6);
+}
+
+.components > .box:nth-child(5) > .header {
+    background-image: linear-gradient(to bottom right, #34d399, #10b981);
+}
+
+.components > .optional {
+    background: repeating-linear-gradient(
+        135deg,
+        #f1f1f1,
+        #f1f1f1 25px,
+        #e3e3e3 25px,
+        #e3e3e3 50px
+    );
+}
+
+.components > .box > .header {
+    border-style: solid;
+    border-width: 1px;
+    border-radius: 0.5rem;
+    border-color: rgb(55 65 81);
+    padding: 0.5rem;
+    text-align: center;
+    margin-bottom: 0.5rem;
+    font-weight: bold;
+    color: white;
+}
+
+.sidebar p {
+    font-size: 100% !important;
+}
+
+.training-arguments {
+    background-color: #f3f6f6;
+    border: 1px solid #e1e4e5;
+}
+
+.training-arguments > .header {
+    font-weight: 700;
+    padding: 6px 12px;
+    background: #e1e4e5;
+}
+
+.training-arguments > .table {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(15em, 1fr));
+}
+
+.training-arguments > .table > a {
+    padding: 0.5rem;
+    border: 1px solid #e1e4e5;
+}

docs/_themes/sphinx_rtd_theme/__init__.py

@@ -8,7 +8,6 @@ from os import path
 
 import sphinx
 
-
 __version__ = "0.5.0"
 __version_full__ = __version__
 

docs/_themes/sphinx_rtd_theme/footer.html

@@ -24,9 +24,6 @@
         © {% trans %}Copyright{% endtrans %} {{ copyright }}
       {%- endif %}
     {%- endif %}
-
-       &bull; <a href="/docs/contact.html">Contact</a>
-
     {%- if build_id and build_url %}
       <span class="build">
         {# Translators: Build is a noun, not a verb #}

docs/_themes/sphinx_rtd_theme/layout.html

@@ -121,8 +121,12 @@
             </a>
 
             <div style="display: flex; justify-content: center;">
-              <div id="twitter-button">
+              <!-- This snippet adds a "Follow SBERT on Twitter" button. I'll remove it as Nils doesn't post about SBERT anmymore -->
+              <!-- <div id="twitter-button">
                 <a href="https://twitter.com/Nils_Reimers" target="_blank" title="Follow SBERT on Twitter"><img src="/_static/Twitter_Logo_White.svg" height="20" style="margin: 0px 10px 0px -10px;"> </a>
+              </div> -->
+              <div id="hf-button">
+                <a href="https://huggingface.co/models?library=sentence-transformers" target="_blank" title="See all Sentence Transformer models"><img src="{{ pathto('_static/hf-logo.svg', 1) }}" style="margin: 0px 10px 0px -10px; padding: 0px; height: 28px; width: 28px;"></a>
               </div>
               <div id="github-button"></div>
             </div>

docs/_themes/sphinx_rtd_theme/search.html

@@ -5,22 +5,23 @@
     Template for the search page.
 
     :copyright: Copyright 2007-2013 by the Sphinx team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
+    :license: BSD, see https://github.com/sphinx-doc/sphinx/blob/master/LICENSE for details.
 #}
 {%- extends "layout.html" %}
 {% set title = _('Search') %}
 {% set display_vcs_links = False %}
 {%- block scripts %}
     {{ super() }}
-    <script type="text/javascript" src="{{ pathto('_static/searchtools.js', 1) }}"></script>
+    <script src="{{ pathto('_static/searchtools.js', 1) }}"></script>
+    <script src="{{ pathto('_static/language_data.js', 1) }}"></script>
 {%- endblock %}
 {% block footer %}
-  <script type="text/javascript">
+  <script>
     jQuery(function() { Search.loadIndex("{{ pathto('searchindex.js', 1) }}"); });
   </script>
   {# this is used when loading the search index using $.ajax fails,
      such as on Chrome for documents on localhost #}
-  <script type="text/javascript" id="searchindexloader"></script>
+  <script id="searchindexloader"></script>
   {{ super() }}
 {% endblock %}
 {% block body %}

docs/_themes/sphinx_rtd_theme/theme.conf

@@ -8,7 +8,7 @@ canonical_url =
 analytics_id =
 collapse_navigation = True
 sticky_navigation = True
-navigation_depth = 4
+navigation_depth =
 includehidden = True
 titles_only =
 logo_only =

docs/conf.py

@@ -14,15 +14,19 @@
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
 
-from recommonmark.transform import AutoStructify
+import datetime
+import importlib
+import inspect
 import os
+
+from recommonmark.transform import AutoStructify
 from sphinx.domains import Domain
-import datetime
+
 # -- Project information -----------------------------------------------------
 
-project = "Sentence-Transformers"
-copyright = str(datetime.datetime.now().year) + ", Nils Reimers"
-author = "Nils Reimers"
+project = "Sentence Transformers"
+copyright = str(datetime.datetime.now().year)
+author = "Nils Reimers, Tom Aarsen"
 
 
 # -- General configuration ---------------------------------------------------
@@ -30,7 +34,16 @@ author = "Nils Reimers"
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = ["sphinx.ext.autodoc", "recommonmark", "sphinx_markdown_tables"]
+extensions = [
+    "sphinx.ext.napoleon",
+    "sphinx.ext.autodoc",
+    "recommonmark",
+    "sphinx_markdown_tables",
+    "sphinx_copybutton",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.linkcode",
+    "sphinx_inline_tabs",
+]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
@@ -38,7 +51,24 @@ templates_path = ["_templates"]
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "nr_examples"]
+exclude_patterns = [
+    "_build",
+    "Thumbs.db",
+    ".DS_Store",
+    "nr_examples",
+    "archived",
+    "dist",
+    "build",
+    "output",
+    "models",
+    "model_card_template.md",
+]
+
+intersphinx_mapping = {
+    "datasets": ("https://huggingface.co/docs/datasets/main/en/", None),
+    "transformers": ("https://huggingface.co/docs/transformers/main/en/", None),
+    "torch": ("https://pytorch.org/docs/stable/", None),
+}
 
 
 # -- Options for HTML output -------------------------------------------------
@@ -49,7 +79,11 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "nr_examples"]
 html_theme = "sphinx_rtd_theme"
 html_theme_path = ["_themes"]
 
-html_theme_options = {"logo_only": True, "canonical_url": "https://www.sbert.net"}
+html_theme_options = {
+    "logo_only": True,
+    "canonical_url": "https://www.sbert.net",
+    "collapse_navigation": False,
+}
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
@@ -78,6 +112,43 @@ html_favicon = "img/favicon.ico"
 autoclass_content = "both"
 
 
+# https://github.com/readthedocs/sphinx-autoapi/issues/202#issuecomment-907582382
+def linkcode_resolve(domain, info):
+    # Non-linkable objects from the starter kit in the tutorial.
+    if domain == "js" or info["module"] == "connect4":
+        return
+
+    assert domain == "py", "expected only Python objects"
+
+    mod = importlib.import_module(info["module"])
+    if "." in info["fullname"]:
+        objname, attrname = info["fullname"].split(".")
+        obj = getattr(mod, objname)
+        try:
+            # object is a method of a class
+            obj = getattr(obj, attrname)
+        except AttributeError:
+            # object is an attribute of a class
+            return None
+    else:
+        obj = getattr(mod, info["fullname"])
+    obj = inspect.unwrap(obj)
+
+    try:
+        file = inspect.getsourcefile(obj)
+        lines = inspect.getsourcelines(obj)
+    except TypeError:
+        # e.g. object is a typing.Union
+        return None
+    file = os.path.relpath(file, os.path.abspath(".."))
+    if not file.startswith("sentence_transformers"):
+        # e.g. object is a typing.NewType
+        return None
+    start, end = lines[1], lines[1] + len(lines[0]) - 1
+
+    return f"https://github.com/UKPLab/sentence-transformers/blob/master/{file}#L{start}-L{end}"
+
+
 class GithubURLDomain(Domain):
     """
     Resolve .py links to their respective Github URL

docs/cross_encoder/pretrained_models.md

+# Pretrained Models
+
+We have released various pre-trained Cross Encoder models via our [Cross Encoder Hugging Face organization](https://huggingface.co/models?author=cross-encoder). Additionally, numerous community CrossEncoder models have been publicly released on the Hugging Face Hub.
+
+Each of these models can be easily downloaded and used like so:
+
+```python
+from sentence_transformers import CrossEncoder
+import torch
+
+model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2", default_activation_function=torch.nn.Sigmoid())
+scores = model.predict([
+    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
+    ("How many people live in Berlin?", "Berlin is well known for its museums."),
+])
+# => array([0.9998173 , 0.01312432], dtype=float32)
+```
+
+Cross-Encoders require text pairs as inputs and output a score 0...1 (if the Sigmoid activation function is used). They do not work for individual sentences and they don't compute embeddings for individual texts.
+
+## MS MARCO
+[MS MARCO Passage Retrieval](https://github.com/microsoft/MSMARCO-Passage-Ranking) is a large dataset with real user queries from Bing search engine with annotated relevant text passages.
+
+```eval_rst
+.. note::
+    You can initialize these models with ``default_activation_function=torch.nn.Sigmoid()`` to force the model to return scores between 0 and 1. Otherwise, the raw value can reasonably range between -10 and 10.
+```
+
+- [cross-encoder/ms-marco-TinyBERT-L-2-v2](https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L-2) - MRR@10 on MS Marco Dev Set: 32.56
+- [cross-encoder/ms-marco-MiniLM-L-2-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-2-v2) - MRR@10 on MS Marco Dev Set: 34.85
+- [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) - MRR@10 on MS Marco Dev Set: 37.70
+- [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) - MRR@10 on MS Marco Dev Set: 39.01
+- [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2) - MRR@10 on MS Marco Dev Set: 39.02
+
+For details on the usage, see [Retrieve & Re-Rank](../../examples/applications/retrieve_rerank/README.md) or [MS MARCO Cross-Encoders](../pretrained-models/ce-msmarco.md).
+
+## SQuAD (QNLI)
+
+QNLI is based on the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) ([HF](https://huggingface.co/datasets/rajpurkar/squad)) and was introduced by the [GLUE Benchmark](https://arxiv.org/abs/1804.07461) ([HF](https://huggingface.co/datasets/nyu-mll/glue)). Given a passage from Wikipedia, annotators created questions that are answerable by that passage.
+
+- [cross-encoder/qnli-distilroberta-base](https://huggingface.co/cross-encoder/qnli-distilroberta-base) - Accuracy on QNLI dev set: 90.96
+- [cross-encoder/qnli-electra-base](https://huggingface.co/cross-encoder/qnli-electra-base) - Accuracy on QNLI dev set: 93.21
+
+## STSbenchmark
+The following models can be used like this:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder("cross-encoder/stsb-roberta-base")
+scores = model.predict([("It's a wonderful day outside.", "It's so sunny today!"), ("It's a wonderful day outside.", "He drove to work earlier.")])
+# => array([0.60443085, 0.00240758], dtype=float32)
+```
+
+They return a score  0...1 indicating the semantic similarity of the given sentence pair.
+- [cross-encoder/stsb-TinyBERT-L-4](https://huggingface.co/cross-encoder/stsb-TinyBERT-L-4) - STSbenchmark test performance: 85.50
+- [cross-encoder/stsb-distilroberta-base](https://huggingface.co/cross-encoder/stsb-distilroberta-base) - STSbenchmark test performance: 87.92
+- [cross-encoder/stsb-roberta-base](https://huggingface.co/cross-encoder/stsb-roberta-base) - STSbenchmark test performance: 90.17
+- [cross-encoder/stsb-roberta-large](https://huggingface.co/cross-encoder/stsb-roberta-large) - STSbenchmark test performance: 91.47 
+
+## Quora Duplicate Questions
+These models have been trained on the [Quora duplicate questions dataset](https://huggingface.co/datasets/sentence-transformers/quora-duplicates). They can used like the STSb models and give a score 0...1 indicating the probability that two questions are duplicate questions.
+
+- [cross-encoder/quora-distilroberta-base](https://huggingface.co/cross-encoder/quora-distilroberta-base) - Average Precision dev set: 87.48
+- [cross-encoder/quora-roberta-base](https://huggingface.co/cross-encoder/quora-roberta-base) - Average Precision dev set: 87.80
+- [cross-encoder/quora-roberta-large](https://huggingface.co/cross-encoder/quora-roberta-large) - Average Precision dev set: 87.91
+
+```eval_rst
+.. note::
+    The model don't work for question similarity. The question *How to learn Java* and *How to learn Python* will get a low score, as these questions are not duplicates. For question similarity, the respective bi-encoder trained on the Quora dataset yields much more meaningful results.
+```
+
+## NLI
+Given two sentences, are these contradicting each other, entailing one the other or are these netural? The following models were trained on the [SNLI](https://huggingface.co/datasets/stanfordnlp/snli) and [MultiNLI](https://huggingface.co/datasets/nyu-mll/multi_nli) datasets.
+- [cross-encoder/nli-deberta-v3-base](https://huggingface.co/cross-encoder/nli-deberta-v3-base) - Accuracy on MNLI mismatched set: 90.04
+- [cross-encoder/nli-deberta-base](https://huggingface.co/cross-encoder/nli-deberta-base) - Accuracy on MNLI mismatched set: 88.08
+- [cross-encoder/nli-deberta-v3-xsmall](https://huggingface.co/cross-encoder/nli-deberta-v3-xsmall) - Accuracy on MNLI mismatched set:  87.77
+- [cross-encoder/nli-deberta-v3-small](https://huggingface.co/cross-encoder/nli-deberta-v3-small) - Accuracy on MNLI mismatched set: 87.55
+- [cross-encoder/nli-roberta-base](https://huggingface.co/cross-encoder/nli-roberta-base) - Accuracy on MNLI mismatched set: 87.47
+- [cross-encoder/nli-MiniLM2-L6-H768](https://huggingface.co/cross-encoder/nli-MiniLM2-L6-H768) - Accuracy on MNLI mismatched set: 86.89  
+- [cross-encoder/nli-distilroberta-base](https://huggingface.co/cross-encoder/nli-distilroberta-base) - Accuracy on MNLI mismatched set: 83.98
+
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder("cross-encoder/nli-deberta-v3-base")
+scores = model.predict([
+    ("A man is eating pizza", "A man eats something"),
+    ("A black race car starts up in front of a crowd of people.", "A man is driving down a lonely road."),
+])
+
+# Convert scores to labels
+label_mapping = ["contradiction", "entailment", "neutral"]
+labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
+# => ['entailment', 'contradiction']
+```
+
+## Community Models
+
+Some notable models from the Community include:
+
+- [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)
+- [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)
+- [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)
+- [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)
+- [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise)
+- [jinaai/jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en)
+- [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
+- [mixedbread-ai/mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1)
+- [mixedbread-ai/mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1)
+- [mixedbread-ai/mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1)
+- [maidalun1020/bce-reranker-base_v1](https://huggingface.co/maidalun1020/bce-reranker-base_v1)
\ No newline at end of file

docs/cross_encoder/training/examples.md

+
+# Training Examples
+
+See the following examples how to train Cross-Encoders:
+
+- [training_stsbenchmark.py](../../../examples/training/cross-encoder/training_stsbenchmark.py) - Example how to train for Semantic Textual Similarity (STS) on the STS benchmark dataset.
+- [training_quora_duplicate_questions.py](../../../examples/training/cross-encoder/training_quora_duplicate_questions.py) - Example how to train a Cross-Encoder to predict if two questions are duplicates. Uses Quora Duplicate Questions as training dataset.
+- [training_nli.py](../../../examples/training/cross-encoder/training_nli.py) - Example for a multilabel classification task for Natural Language Inference (NLI) task.
+
+```eval_rst
+.. toctree::
+   :maxdepth: 1
+   :caption: Supervised Learning
+
+   ../../../examples/training/ms_marco/cross_encoder_README
+```
\ No newline at end of file

docs/cross_encoder/training_overview.md

+
+# Training Overview
+
+```eval_rst
+.. note::
+    The CrossEncoder training approach has not been updated in v3.0 when `training Sentence Transformer models <../sentence_transformer/training_overview.html>`_ was improved. Improving training CrossEncoders is planned for a future major update.
+```
+
+The `CrossEncoder` class is a wrapper around Huggingface `AutoModelForSequenceClassification`, but with some methods to make training and predicting scores a little bit easier. The saved models are 100% compatible with Huggingface and can also be loaded with their classes.
+
+First, you need some sentence pair data. You can either have a continuous score, like:
+
+```eval_rst
+
+.. sidebar:: Documentation
+
+    - :class:`~sentence_transformers.readers.InputExample`
+```
+
+```python
+from sentence_transformers import InputExample
+
+train_samples = [
+    InputExample(texts=["sentence1", "sentence2"], label=0.3),
+    InputExample(texts=["Another", "pair"], label=0.8),
+]
+```
+
+Or you have distinct classes as in the [training_nli.py](../../examples/training/cross-encoder/training_nli.py) example:
+```python
+from sentence_transformers import InputExample
+
+label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
+train_samples = [
+    InputExample(texts=["sentence1", "sentence2"], label=label2int["neutral"]),
+    InputExample(texts=["Another", "pair"], label=label2int["entailment"]),
+]
+```
+
+Then, you define the base model and the number of labels. You can take any [Hugging Face pre-trained model](https://huggingface.co/models) that is compatible with AutoModel:
+```
+model = CrossEncoder('distilroberta-base', num_labels=1)
+```
+
+For binary tasks and tasks with continuous scores (like STS), we set num_labels=1. For classification tasks, we set it to the number of labels we have.
+
+```eval_rst
+
+We start the training by calling :meth:`CrossEncoder.fit <sentence_transformers.cross_encoder.CrossEncoder.fit>`:
+
+.. sidebar:: Documentation
+
+    - :class:`~sentence_transformers.cross_encoder.CrossEncoder`
+    - :meth:`CrossEncoder.fit <sentence_transformers.cross_encoder.CrossEncoder.fit>`
+
+::
+
+    model.fit(
+        train_dataloader=train_dataloader,
+        evaluator=evaluator,
+        epochs=num_epochs,
+        warmup_steps=warmup_steps,
+        output_path=model_save_path,
+    )
+```
\ No newline at end of file

docs/cross_encoder/usage/usage.rst

+
+Usage
+=====
+
+Characteristics of Cross Encoder (a.k.a reranker) models:
+
+1. Calculates a **similarity score** given **pairs of texts**.
+2. Generally provides **superior performance** compared to a Sentence Transformer (a.k.a. bi-encoder) model.
+3. Often **slower** than a Sentence Transformer model, as it requires computation for each pair rather than each text.
+4. Due to the previous 2 characteristics, Cross Encoders are often used to **re-rank the top-k results** from a Sentence Transformer model.
+
+Once you have installed `installed <installation.md>`_ Sentence Transformers, you can easily use Cross Encoder models:
+
+.. sidebar:: Documentation
+
+   1. :class:`~sentence_transformers.cross_encoder.CrossEncoder`
+   2. :meth:`CrossEncoder.predict <sentence_transformers.cross_encoder.CrossEncoder.predict>`
+   3. :meth:`CrossEncoder.rank <sentence_transformers.cross_encoder.CrossEncoder.rank>`
+
+   .. note::
+      MS Marco models return logits rather than scores between 0 and 1. Load the :class:`~sentence_transformers.cross_encoder.CrossEncoder` with ``default_activation_function=torch.nn.Sigmoid()`` to get scores between 0 and 1. This does not affect the ranking.
+
+::
+
+   from sentence_transformers import CrossEncoder
+   
+   # 1. Load a pre-trained CrossEncoder model
+   model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
+
+   # 2. Predict scores for a pair of sentences
+   scores = model.predict([
+       ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
+       ("How many people live in Berlin?", "Berlin is well known for its museums."),
+   ])
+   # => array([ 8.607138 , -4.3200774], dtype=float32)
+   
+   # 3. Rank a list of passages for a query
+   query = "How many people live in Berlin?"
+   passages = [
+       "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.",
+       "Berlin is well known for its museums.",
+       "In 2014, the city state Berlin had 37,368 live births (+6.6%), a record number since 1991.",
+       "The urban area of Berlin comprised about 4.1 million people in 2014, making it the seventh most populous urban area in the European Union.",
+       "The city of Paris had a population of 2,165,423 people within its administrative city limits as of January 1, 2019",
+       "An estimated 300,000-420,000 Muslims reside in Berlin, making up about 8-11 percent of the population.",
+       "Berlin is subdivided into 12 boroughs or districts (Bezirke).",
+       "In 2015, the total labour force in Berlin was 1.85 million.",
+       "In 2013 around 600,000 Berliners were registered in one of the more than 2,300 sport and fitness clubs.",
+       "Berlin has a yearly total of about 135 million day visitors, which puts it in third place among the most-visited city destinations in the European Union.",
+   ]
+   ranks = model.rank(query, passages)
+   
+   # Print the scores
+   print("Query:", query)
+   for rank in ranks:
+       print(f"{rank['score']:.2f}\t{passages[rank['corpus_id']]}")
+   """
+   Query: How many people live in Berlin?
+   8.92    The urban area of Berlin comprised about 4.1 million people in 2014, making it the seventh most populous urban area in the European Union.
+   8.61    Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.
+   8.24    An estimated 300,000-420,000 Muslims reside in Berlin, making up about 8-11 percent of the population.
+   7.60    In 2014, the city state Berlin had 37,368 live births (+6.6%), a record number since 1991.
+   6.35    In 2013 around 600,000 Berliners were registered in one of the more than 2,300 sport and fitness clubs.
+   5.42    Berlin has a yearly total of about 135 million day visitors, which puts it in third place among the most-visited city destinations in the European Union.
+   3.45    In 2015, the total labour force in Berlin was 1.85 million.
+   0.33    Berlin is subdivided into 12 boroughs or districts (Bezirke).
+   -4.24   The city of Paris had a population of 2,165,423 people within its administrative city limits as of January 1, 2019
+   -4.32   Berlin is well known for its museums.
+   """
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Tasks
+
+   ../../../examples/applications/retrieve_rerank/README

docs/img/hf-logo.svg

+<svg width="256" height="256" viewBox="0 0 256 256" fill="none" xmlns="http://www.w3.org/2000/svg">
+<g filter="url(#filter0_d_3_42)">
+<path d="M230.721 172.7C230.183 170.674 229.313 168.75 228.146 167.008C228.396 166.091 228.587 165.159 228.714 164.217C229.543 158.241 227.471 152.77 223.567 148.537C221.452 146.225 219.185 144.698 216.784 143.761C218.36 137.018 219.157 130.117 219.161 123.193C219.161 120.03 218.982 116.932 218.682 113.88C218.529 112.354 218.333 110.827 218.115 109.32C217.428 104.846 216.408 100.431 215.064 96.1103C214.169 93.2536 213.166 90.4617 212.01 87.7359C210.281 83.678 208.262 79.7498 205.969 75.9816C204.465 73.4749 202.827 71.0509 201.062 68.72C200.19 67.5423 199.296 66.3859 198.358 65.2744C195.58 61.898 192.561 58.7275 189.325 55.7878C188.257 54.8041 187.144 53.8463 186.01 52.9261C184.898 51.9889 183.742 51.0946 182.586 50.2218C180.253 48.477 177.811 46.8414 175.324 45.3148C161.543 36.9453 145.382 32.1456 128.109 32.1456C77.8172 32.1456 37.0566 72.9071 37.0566 123.196C37.0552 130.208 37.8676 137.196 39.4776 144.02C37.3178 144.958 35.2465 146.42 33.3273 148.535C29.4235 152.766 27.3514 158.217 28.1804 164.193C28.3058 165.142 28.4952 166.082 28.7474 167.006C27.5812 168.749 26.7115 170.673 26.1736 172.7C24.9743 177.261 25.3687 181.374 26.8935 184.978C25.2363 189.688 25.6504 194.704 27.8093 199.065C29.3797 202.25 31.6263 204.714 34.396 206.916C37.6889 209.534 41.8109 211.758 46.7833 213.892C52.7154 216.422 59.9559 218.799 63.2488 219.671C71.7548 221.873 79.9108 223.269 88.1772 223.337C99.9539 223.446 110.096 220.677 117.357 213.59C120.924 214.027 124.515 214.246 128.109 214.244C131.906 214.236 135.699 213.997 139.467 213.529C146.711 220.661 156.892 223.455 168.712 223.343C176.977 223.277 185.133 221.881 193.617 219.676C196.932 218.804 204.17 216.427 210.105 213.897C215.077 211.76 219.199 209.536 222.514 206.922C225.263 204.719 227.508 202.256 229.079 199.071C231.26 194.709 231.652 189.693 230.017 184.983C231.527 181.379 231.92 177.257 230.721 172.7ZM222.281 184.673C223.952 187.844 224.059 191.427 222.585 194.764C220.349 199.821 214.795 203.805 204.008 208.082C197.3 210.742 191.158 212.443 191.104 212.458C182.232 214.759 174.208 215.928 167.262 215.928C155.76 215.928 147.201 212.754 141.773 206.486C132.595 208.05 123.222 208.103 114.026 206.644C108.591 212.808 100.081 215.928 88.6752 215.928C81.7291 215.928 73.7054 214.759 64.8335 212.458C64.779 212.443 58.6394 210.742 51.9293 208.082C41.1428 203.805 35.5873 199.824 33.3523 194.764C31.8776 191.427 31.9849 187.844 33.6555 184.673C33.8102 184.378 33.9757 184.091 34.1527 183.813C33.1514 182.309 32.4799 180.61 32.1821 178.828C31.8843 177.045 31.967 175.22 32.4249 173.472C33.0885 170.949 34.4604 168.851 36.3224 167.344C35.4257 165.871 34.8373 164.23 34.5927 162.522C34.0561 158.808 35.2885 155.1 38.0627 152.076C40.2216 149.723 43.2749 148.428 46.6554 148.428H46.7449C44.1964 140.26 42.9043 131.751 42.9127 123.193C42.9127 76.5217 80.7492 38.6838 127.427 38.6838C174.104 38.6838 211.94 76.5182 211.94 123.193C211.947 131.773 210.646 140.304 208.081 148.492C208.489 148.452 208.889 148.432 209.282 148.431C212.662 148.431 215.716 149.726 217.874 152.079C220.647 155.1 221.881 158.811 221.344 162.525C221.1 164.233 220.511 165.873 219.615 167.347C221.477 168.854 222.849 170.952 223.512 173.475C223.97 175.223 224.053 177.048 223.755 178.83C223.457 180.612 222.786 182.312 221.784 183.816C221.961 184.091 222.129 184.378 222.281 184.673Z" fill="white"/>
+</g>
+<path d="M221.784 183.816C222.786 182.312 223.457 180.613 223.755 178.831C224.053 177.048 223.97 175.223 223.512 173.475C222.848 170.952 221.476 168.854 219.615 167.347C220.511 165.874 221.1 164.233 221.344 162.525C221.881 158.811 220.648 155.103 217.874 152.079C215.716 149.726 212.662 148.431 209.282 148.431C208.889 148.431 208.489 148.452 208.081 148.492C210.643 140.304 211.942 131.774 211.933 123.195C211.933 76.523 174.097 38.6849 127.424 38.6849C80.7505 38.6849 42.9098 76.5194 42.9098 123.195C42.9014 131.752 44.1936 140.261 46.742 148.43H46.6525C43.272 148.43 40.2188 149.724 38.0599 152.077C35.2875 155.098 34.0533 158.81 34.5899 162.523C34.8344 164.231 35.4228 165.872 36.3195 167.346C34.4575 168.852 33.0856 170.95 32.422 173.473C31.9643 175.222 31.8818 177.047 32.1801 178.83C32.4784 180.613 33.1505 182.312 34.1526 183.816C33.9737 184.094 33.81 184.381 33.6553 184.676C31.9847 187.847 31.8774 191.43 33.3521 194.767C35.5879 199.824 41.1426 203.808 51.9291 208.085C58.6365 210.745 64.7788 212.446 64.8334 212.461C73.7052 214.762 81.729 215.931 88.6751 215.931C100.081 215.931 108.591 212.811 114.026 206.647C123.222 208.106 132.594 208.052 141.773 206.489C147.201 212.757 155.76 215.931 167.262 215.931C174.208 215.931 182.232 214.762 191.103 212.461C191.158 212.446 197.298 210.745 204.008 208.085C214.795 203.808 220.35 199.824 222.585 194.767C224.059 191.43 223.952 187.847 222.281 184.676C222.129 184.379 221.961 184.091 221.784 183.816ZM110.137 196.997C109.692 197.778 109.192 198.576 108.635 199.391C107.23 201.448 105.382 203.02 103.237 204.188C99.1363 206.424 93.9472 207.205 88.6751 207.205C80.3464 207.205 71.8082 205.256 67.0227 204.015C66.7874 203.954 37.6887 195.735 41.3734 188.739C41.9922 187.562 43.0136 187.092 44.2978 187.092C49.4849 187.092 58.9299 194.816 62.9892 194.816C63.8961 194.816 64.5355 194.43 64.7967 193.488C66.5263 187.284 38.5035 184.676 40.8636 175.692C41.2803 174.102 42.4099 173.456 43.9982 173.456C50.8559 173.455 66.2473 185.516 69.4668 185.516C69.7137 185.516 69.8908 185.443 69.9865 185.291C70.0008 185.268 70.0151 185.246 70.0285 185.222C71.539 182.727 70.6724 180.913 60.3206 174.573C59.9986 174.376 59.6674 174.175 59.327 173.968C47.9359 167.074 39.9406 162.925 44.4883 157.975C45.0115 157.404 45.7529 157.151 46.6535 157.151C47.7222 157.151 49.0154 157.508 50.4392 158.108C56.4553 160.645 64.7931 167.564 68.2765 170.581C69.3032 171.476 69.9087 172.022 69.9087 172.022C69.9087 172.022 74.3185 176.608 76.9854 176.608C77.5992 176.608 78.1195 176.366 78.4726 175.768C80.3644 172.58 60.9099 157.838 59.8135 151.755C59.0694 147.634 60.3349 145.546 62.6753 145.546C63.7879 145.546 65.1464 146.02 66.6444 146.971C71.2949 149.922 80.2729 165.35 83.5595 171.352C84.6615 173.363 86.543 174.213 88.2379 174.213C91.6008 174.213 94.2298 170.87 88.5457 166.622C80.003 160.23 83.0007 149.782 87.078 149.139C87.2516 149.111 87.4272 149.097 87.6027 149.097C91.3109 149.097 92.9464 155.486 92.9464 155.486C92.9464 155.486 97.74 167.524 105.975 175.753C113.447 183.222 114.491 189.351 110.137 196.997ZM136.766 198.407L136.339 198.458C136.096 198.486 135.854 198.514 135.611 198.541C135.228 198.581 134.844 198.619 134.459 198.654L134.084 198.688L133.741 198.717L133.255 198.756C133.076 198.77 132.897 198.783 132.718 198.795L132.182 198.83L132.063 198.838C131.923 198.846 131.783 198.855 131.641 198.862L131.462 198.872C131.296 198.881 131.13 198.889 130.962 198.896L130.381 198.921L129.854 198.939L129.502 198.949H129.323C129.213 198.949 129.104 198.955 128.994 198.956C128.936 198.956 128.878 198.956 128.82 198.956C128.71 198.956 128.601 198.956 128.491 198.961L128.043 198.967C127.835 198.967 127.628 198.967 127.418 198.967C126.927 198.967 126.437 198.962 125.949 198.952L125.553 198.943C125.44 198.943 125.327 198.938 125.216 198.934L124.796 198.922L124.275 198.902L123.805 198.881L123.684 198.876L123.237 198.853C123.112 198.846 122.989 198.84 122.865 198.831L122.576 198.814C122.212 198.791 121.849 198.766 121.487 198.738L121.107 198.707C120.947 198.695 120.787 198.68 120.628 198.666C120.441 198.65 120.254 198.632 120.067 198.614C119.753 198.585 119.44 198.553 119.128 198.519H119.113C123.683 188.324 121.372 178.802 112.137 169.575C106.08 163.526 102.051 154.594 101.215 152.633C99.5234 146.828 95.0449 140.375 87.6081 140.375C86.9793 140.375 86.3514 140.425 85.7304 140.523C82.4721 141.036 79.6237 142.911 77.5919 145.733C75.3961 143.002 73.2624 140.831 71.3315 139.605C68.4223 137.76 65.5184 136.824 62.6887 136.824C59.1579 136.824 56.0019 138.274 53.8018 140.904L53.7464 140.971C53.7043 140.798 53.6641 140.625 53.6229 140.451L53.6176 140.428C53.4942 139.895 53.3749 139.359 53.2599 138.819C53.0709 137.919 52.8982 137.013 52.742 136.1C52.6824 135.742 52.6228 135.381 52.5632 135.016C52.5632 135.004 52.5632 134.992 52.5578 134.98C52.5364 134.843 52.5158 134.705 52.4952 134.568C52.4827 134.487 52.4702 134.404 52.4585 134.323C52.438 134.183 52.4174 134.042 52.3977 133.901C52.3718 133.717 52.3459 133.532 52.3217 133.348C52.2976 133.163 52.2725 132.978 52.2493 132.793C52.226 132.608 52.2037 132.423 52.1813 132.238C52.1589 132.053 52.1402 131.885 52.1205 131.709L52.1151 131.665C52.0752 131.299 52.037 130.933 52.0006 130.564C51.9666 130.211 51.9351 129.857 51.9058 129.503C51.8978 129.414 51.8906 129.319 51.8835 129.226C51.871 129.07 51.8594 128.912 51.8477 128.754C51.8379 128.625 51.8289 128.495 51.8209 128.365C51.8209 128.334 51.8164 128.304 51.8146 128.275C51.803 128.102 51.7914 127.927 51.7815 127.753C51.7699 127.564 51.76 127.376 51.7502 127.187C51.7404 126.998 51.7306 126.81 51.7225 126.62C51.7154 126.455 51.7082 126.29 51.7019 126.124L51.6966 125.974C51.6912 125.822 51.6858 125.669 51.6814 125.517L51.6706 125.128C51.6706 124.973 51.6635 124.818 51.6608 124.663C51.6581 124.508 51.6545 124.338 51.6528 124.174C51.651 124.01 51.6528 123.848 51.6483 123.685C51.6438 123.521 51.6483 123.358 51.6483 123.195C51.6483 81.3422 85.5792 47.4114 127.436 47.4114C169.292 47.4114 203.222 81.3403 203.222 123.195C203.222 123.358 203.222 123.521 203.222 123.685C203.222 123.848 203.222 124.011 203.222 124.174C203.222 124.337 203.217 124.501 203.214 124.663C203.214 124.798 203.208 124.931 203.204 125.068C203.204 125.188 203.199 125.309 203.195 125.425C203.195 125.578 203.186 125.731 203.181 125.884V125.896C203.174 126.075 203.167 126.252 203.16 126.427C203.153 126.582 203.147 126.738 203.139 126.893L203.134 127.003C203.126 127.168 203.116 127.333 203.107 127.499C203.1 127.631 203.093 127.761 203.084 127.892C203.036 128.686 202.976 129.478 202.905 130.267C202.892 130.405 202.879 130.544 202.866 130.683C202.866 130.687 202.866 130.691 202.866 130.696C202.849 130.87 202.832 131.044 202.813 131.218C202.798 131.355 202.784 131.492 202.768 131.629C202.739 131.897 202.709 132.166 202.679 132.433L202.628 132.84C202.607 132.999 202.586 133.159 202.565 133.319C202.542 133.493 202.519 133.668 202.493 133.841C202.467 134.036 202.438 134.23 202.409 134.424L202.34 134.883C202.313 135.057 202.285 135.229 202.258 135.403C202.23 135.576 202.201 135.748 202.168 135.92C202.135 136.093 202.109 136.265 202.079 136.437C202.019 136.781 201.956 137.125 201.89 137.468C201.826 137.796 201.761 138.124 201.695 138.451C201.657 138.636 201.619 138.821 201.58 139.005C201.544 139.175 201.507 139.343 201.47 139.512C201.434 139.681 201.395 139.851 201.357 140.02C199.224 137.947 196.399 136.818 193.284 136.818C190.457 136.818 187.55 137.753 184.641 139.598C182.711 140.824 180.578 142.996 178.381 145.726C176.346 142.904 173.498 141.029 170.242 140.516C169.621 140.418 168.993 140.368 168.364 140.368C160.925 140.368 156.45 146.821 154.757 152.626C153.917 154.587 149.887 163.519 143.825 169.577C134.596 178.775 132.268 188.254 136.766 198.407ZM215.007 177.998C214.997 178.027 214.987 178.055 214.977 178.087C214.956 178.145 214.934 178.201 214.911 178.258C214.876 178.342 214.838 178.425 214.798 178.509C214.771 178.565 214.743 178.62 214.714 178.674C214.64 178.812 214.559 178.948 214.47 179.082C214.362 179.243 214.245 179.403 214.117 179.561C214.055 179.638 213.989 179.716 213.921 179.793C213.875 179.845 213.831 179.897 213.779 179.948C213.708 180.024 213.634 180.1 213.559 180.175C212.213 181.509 210.161 182.679 207.841 183.752C207.578 183.871 207.311 183.99 207.042 184.11L206.774 184.229C206.595 184.308 206.416 184.386 206.228 184.463C206.049 184.541 205.863 184.619 205.677 184.695L205.119 184.925C203.814 185.462 202.477 185.974 201.173 186.479L200.615 186.696L200.064 186.912C199.697 187.055 199.335 187.198 198.979 187.341L198.448 187.555L197.926 187.768L197.67 187.876C197.499 187.947 197.332 188.018 197.165 188.089C193.328 189.736 190.567 191.411 191.147 193.489C191.163 193.548 191.181 193.604 191.201 193.659C191.253 193.813 191.324 193.958 191.413 194.095C191.465 194.176 191.525 194.253 191.592 194.323C192.274 195.032 193.515 194.92 195.08 194.357C195.206 194.311 195.336 194.262 195.468 194.211C195.557 194.177 195.647 194.141 195.736 194.104L195.872 194.048C196.23 193.896 196.609 193.726 196.996 193.542C197.093 193.496 197.191 193.452 197.289 193.401C199.203 192.465 201.372 191.205 203.524 190.058C203.854 189.879 204.185 189.707 204.514 189.537C205.062 189.254 205.607 188.983 206.142 188.733C208.18 187.774 210.096 187.094 211.636 187.094C212.359 187.094 212.997 187.242 213.529 187.582L213.618 187.641C213.675 187.681 213.732 187.723 213.786 187.768C213.986 187.933 214.163 188.122 214.312 188.333C214.356 188.395 214.401 188.461 214.441 188.528C214.482 188.595 214.522 188.666 214.561 188.739C215.322 190.184 214.685 191.68 213.194 193.147C211.763 194.556 209.537 195.937 207.007 197.215C206.819 197.31 206.631 197.405 206.44 197.498C198.91 201.196 189.049 203.981 188.912 204.016C186.284 204.697 182.526 205.591 178.292 206.26L177.666 206.358L177.563 206.373C177.337 206.407 177.11 206.44 176.882 206.472C176.635 206.507 176.387 206.541 176.138 206.574C175.655 206.639 175.167 206.698 174.676 206.753L174.586 206.763C172.831 206.962 171.032 207.107 169.228 207.169H169.202C168.554 207.192 167.907 207.204 167.259 207.204C167.155 207.204 167.052 207.204 166.948 207.204H166.859C166.744 207.204 166.628 207.204 166.512 207.204C166.136 207.199 165.761 207.189 165.387 207.175C164.772 207.153 164.16 207.118 163.553 207.07C163.53 207.07 163.505 207.07 163.482 207.064C163.251 207.046 163.02 207.026 162.791 207.003C162.668 206.992 162.547 206.979 162.425 206.965C162.059 206.926 161.694 206.882 161.333 206.833C161.093 206.8 160.852 206.765 160.618 206.726C160.383 206.688 160.134 206.647 159.893 206.605C159.783 206.585 159.673 206.564 159.564 206.543L159.539 206.538C159.19 206.471 158.845 206.399 158.503 206.319C158.303 206.274 158.104 206.23 157.907 206.176L157.788 206.146C157.69 206.122 157.595 206.096 157.498 206.07L157.445 206.056L157.137 205.966C157.025 205.935 156.913 205.901 156.801 205.868L156.762 205.857L156.471 205.768C156.361 205.734 156.251 205.698 156.142 205.662C156.053 205.633 155.964 205.602 155.874 205.573L155.677 205.504C155.487 205.437 155.298 205.368 155.111 205.296L154.933 205.226L154.786 205.168C154.5 205.053 154.219 204.934 153.941 204.81L153.756 204.72L153.725 204.706C153.659 204.675 153.594 204.644 153.528 204.617C153.399 204.555 153.271 204.491 153.144 204.426L153.105 204.407L152.921 204.31C152.711 204.199 152.504 204.085 152.301 203.966C152.186 203.9 152.072 203.833 151.96 203.764L151.788 203.658C151.699 203.604 151.617 203.55 151.532 203.494L151.308 203.346C151.227 203.291 151.147 203.236 151.067 203.18L150.923 203.077C150.771 202.969 150.622 202.857 150.476 202.742C150.398 202.682 150.32 202.621 150.243 202.563C150.151 202.488 150.058 202.412 149.967 202.335C149.89 202.272 149.815 202.206 149.74 202.14L149.734 202.135C149.653 202.064 149.574 201.993 149.495 201.92C149.417 201.849 149.339 201.777 149.263 201.704L149.254 201.695C149.175 201.619 149.096 201.542 149.019 201.463C148.942 201.385 148.863 201.307 148.788 201.227C148.713 201.148 148.636 201.067 148.562 200.984C148.488 200.902 148.42 200.827 148.35 200.746L148.327 200.719C148.259 200.641 148.192 200.562 148.126 200.481C147.983 200.31 147.844 200.135 147.71 199.956C147.575 199.776 147.443 199.592 147.314 199.405C147.273 199.343 147.231 199.282 147.191 199.221C147.108 199.099 147.027 198.978 146.948 198.857C146.868 198.736 146.79 198.615 146.712 198.493C146.634 198.374 146.558 198.255 146.484 198.136C146.445 198.076 146.408 198.017 146.373 197.957C146.302 197.844 146.234 197.73 146.166 197.618C146.157 197.603 146.148 197.587 146.138 197.572C146.073 197.462 146.009 197.354 145.947 197.245C145.911 197.186 145.878 197.128 145.845 197.066C145.812 197.004 145.774 196.941 145.739 196.878C145.719 196.845 145.701 196.812 145.682 196.779C145.664 196.746 145.658 196.736 145.647 196.715C145.58 196.595 145.514 196.474 145.45 196.352C145.42 196.298 145.391 196.244 145.36 196.192L145.271 196.019L145.181 195.848C145.123 195.733 145.066 195.616 145.01 195.501C144.981 195.444 144.953 195.387 144.927 195.33C144.869 195.208 144.812 195.086 144.757 194.966C144.734 194.917 144.712 194.868 144.692 194.819C144.64 194.705 144.59 194.593 144.543 194.48C144.519 194.423 144.495 194.367 144.472 194.311C144.426 194.198 144.383 194.086 144.337 193.975C144.315 193.921 144.293 193.868 144.274 193.814C144.187 193.588 144.106 193.364 144.031 193.14C144.011 193.085 143.992 193.029 143.975 192.975C143.942 192.874 143.91 192.775 143.88 192.675C143.825 192.5 143.775 192.325 143.729 192.152C143.713 192.097 143.699 192.043 143.685 191.988C143.642 191.825 143.603 191.662 143.567 191.499C143.542 191.391 143.519 191.283 143.498 191.175C143.487 191.12 143.476 191.065 143.467 191.012C143.446 190.903 143.427 190.796 143.41 190.689C143.383 190.528 143.36 190.367 143.34 190.206C143.332 190.153 143.326 190.1 143.32 190.047C143.315 189.994 143.309 189.94 143.303 189.885C143.281 189.671 143.264 189.459 143.254 189.247C143.254 189.193 143.249 189.139 143.247 189.087C143.242 188.98 143.24 188.875 143.239 188.769C143.183 184.496 145.345 180.388 149.968 175.767C158.203 167.54 162.997 155.501 162.997 155.501C162.997 155.501 163.126 154.996 163.394 154.269C163.431 154.168 163.47 154.064 163.514 153.955C163.564 153.825 163.619 153.69 163.678 153.551C163.756 153.366 163.842 153.174 163.935 152.979C163.969 152.905 164.004 152.832 164.041 152.758C164.054 152.733 164.067 152.708 164.08 152.683C164.104 152.634 164.129 152.583 164.155 152.534C164.298 152.26 164.454 151.983 164.624 151.712C164.67 151.639 164.714 151.567 164.765 151.494C164.863 151.348 164.965 151.205 165.069 151.066C165.121 150.996 165.175 150.928 165.23 150.86C165.319 150.749 165.416 150.639 165.513 150.532C165.552 150.49 165.59 150.448 165.631 150.408C166.108 149.915 166.653 149.513 167.27 149.299L167.348 149.273C167.4 149.256 167.452 149.24 167.505 149.225C167.566 149.209 167.627 149.195 167.69 149.182L167.719 149.176C167.849 149.15 167.981 149.133 168.114 149.124H168.125C168.194 149.124 168.264 149.117 168.335 149.117C168.424 149.117 168.507 149.117 168.594 149.126C168.684 149.134 168.773 149.144 168.863 149.158C169.605 149.276 170.311 149.718 170.919 150.4C170.947 150.431 170.975 150.463 171.001 150.495C171.142 150.66 171.272 150.833 171.393 151.011C171.444 151.084 171.493 151.159 171.54 151.236C171.66 151.428 171.773 151.631 171.88 151.845C171.923 151.934 171.964 152.016 172.004 152.104C172.025 152.149 172.045 152.193 172.064 152.239C172.103 152.329 172.141 152.418 172.178 152.513C172.214 152.602 172.25 152.7 172.284 152.795C172.442 153.242 172.569 153.7 172.662 154.165C172.684 154.272 172.704 154.379 172.723 154.487C172.788 154.878 172.833 155.271 172.857 155.666C172.865 155.781 172.87 155.897 172.873 156.013C172.881 156.286 172.881 156.563 172.873 156.842C172.869 156.949 172.864 157.055 172.856 157.162C172.786 158.174 172.587 159.172 172.265 160.133C172.209 160.3 172.149 160.467 172.086 160.634C172.044 160.745 171.997 160.857 171.952 160.969C171.863 161.19 171.759 161.416 171.65 161.634C171.569 161.799 171.484 161.965 171.392 162.13C171.332 162.24 171.269 162.35 171.206 162.46C171.045 162.734 170.871 163.006 170.684 163.277L170.571 163.439C170.417 163.653 170.254 163.866 170.083 164.078C169.775 164.458 169.446 164.822 169.099 165.167C168.569 165.698 168.001 166.189 167.4 166.637C166.857 167.038 166.344 167.478 165.865 167.955C165.813 168.008 165.761 168.061 165.711 168.114C164.208 169.691 163.858 171.083 164.196 172.138C164.212 172.187 164.229 172.234 164.247 172.28C164.274 172.351 164.305 172.419 164.336 172.486C164.358 172.531 164.382 172.575 164.407 172.617C164.508 172.791 164.628 172.951 164.764 173.097C164.781 173.116 164.799 173.134 164.817 173.152L164.871 173.206C164.925 173.258 164.982 173.309 165.043 173.359L165.103 173.407C165.143 173.438 165.185 173.469 165.227 173.497C165.335 173.573 165.447 173.643 165.563 173.707C165.61 173.732 165.652 173.757 165.705 173.781C165.879 173.866 166.058 173.939 166.242 173.998C166.293 174.015 166.344 174.03 166.396 174.046L166.461 174.063L166.551 174.087L166.628 174.106L166.712 174.124L166.795 174.141L166.874 174.154C166.932 174.164 166.992 174.174 167.052 174.181L167.109 174.19L167.213 174.2L167.277 174.207L167.382 174.214H167.444L167.554 174.22H167.603H167.705H167.752H167.9L167.999 174.214L168.113 174.207L168.252 174.194L168.382 174.179C168.412 174.179 168.442 174.171 168.472 174.165C168.538 174.156 168.605 174.145 168.671 174.132C168.835 174.101 168.997 174.062 169.157 174.015C169.32 173.968 169.481 173.912 169.639 173.849C169.691 173.827 169.745 173.805 169.798 173.782C169.887 173.743 169.977 173.702 170.059 173.658C170.129 173.624 170.198 173.586 170.266 173.547C170.368 173.489 170.47 173.428 170.57 173.361C170.799 173.211 171.015 173.043 171.217 172.858C171.265 172.815 171.312 172.769 171.358 172.725C171.381 172.703 171.403 172.682 171.425 172.658C171.469 172.613 171.514 172.569 171.558 172.52C171.645 172.425 171.731 172.326 171.814 172.221C172.028 171.952 172.218 171.665 172.383 171.363C173.718 168.925 175.991 164.93 178.534 160.849C178.632 160.692 178.73 160.535 178.828 160.378C178.926 160.222 179.026 160.064 179.125 159.907C179.273 159.668 179.423 159.433 179.572 159.199L179.722 158.965C179.822 158.808 179.923 158.651 180.024 158.496C180.427 157.87 180.833 157.252 181.241 156.641L181.546 156.185C182.158 155.278 182.768 154.396 183.373 153.558L183.674 153.143C184.121 152.526 184.569 151.934 185.008 151.376C185.251 151.067 185.491 150.769 185.728 150.482L186.01 150.144C186.057 150.088 186.1 150.032 186.151 149.978C186.244 149.868 186.337 149.761 186.428 149.657C186.474 149.604 186.517 149.552 186.566 149.5C186.658 149.397 186.747 149.296 186.834 149.198C186.878 149.149 186.924 149.1 186.968 149.051C187.103 148.906 187.235 148.767 187.365 148.634C187.455 148.544 187.538 148.455 187.624 148.371C188.131 147.853 188.69 147.388 189.293 146.985L189.433 146.895C189.479 146.865 189.522 146.837 189.573 146.806C189.662 146.75 189.752 146.697 189.848 146.645C192.212 145.303 194.169 145.204 195.296 146.331C195.978 147.013 196.356 148.144 196.335 149.718C196.335 149.787 196.335 149.857 196.33 149.929C196.33 149.954 196.33 149.98 196.33 150.006C196.33 150.078 196.324 150.15 196.318 150.223C196.318 150.313 196.308 150.402 196.299 150.492C196.29 150.581 196.285 150.649 196.276 150.729C196.276 150.751 196.272 150.774 196.268 150.798C196.262 150.867 196.253 150.938 196.243 151.009C196.243 151.03 196.243 151.052 196.235 151.074C196.226 151.147 196.216 151.222 196.204 151.296C196.202 151.317 196.199 151.337 196.194 151.357C196.183 151.447 196.168 151.531 196.152 151.619C196.144 151.669 196.135 151.718 196.126 151.768C196.1 151.91 196.067 152.05 196.026 152.188C195.983 152.333 195.932 152.482 195.873 152.636C195.833 152.737 195.79 152.84 195.743 152.946C195.697 153.051 195.646 153.157 195.592 153.265C195.485 153.481 195.363 153.704 195.229 153.933C195.162 154.047 195.092 154.163 195.019 154.28C194.982 154.338 194.945 154.397 194.907 154.459C194.793 154.638 194.673 154.819 194.549 155.002C194.425 155.185 194.292 155.371 194.15 155.561C193.965 155.813 193.769 156.069 193.564 156.33L193.408 156.527C193.146 156.854 192.871 157.188 192.583 157.529C192.41 157.733 192.233 157.938 192.052 158.146C191.932 158.284 191.81 158.422 191.686 158.562L191.499 158.772C191.247 159.053 190.991 159.336 190.729 159.62L190.532 159.834C190.401 159.977 190.264 160.12 190.132 160.264C190.001 160.407 189.864 160.552 189.726 160.697L189.315 161.13C189.177 161.275 189.038 161.421 188.898 161.566C188.758 161.711 188.619 161.857 188.478 162.002C188.196 162.294 187.913 162.586 187.628 162.878C183.573 167.037 179.301 171.182 177.855 173.766C177.759 173.934 177.671 174.107 177.593 174.285C177.387 174.755 177.301 175.157 177.36 175.482C177.379 175.589 177.416 175.691 177.471 175.785C177.514 175.857 177.561 175.927 177.611 175.993C177.659 176.056 177.711 176.116 177.766 176.172C177.819 176.224 177.875 176.272 177.934 176.316C178.232 176.528 178.591 176.637 178.957 176.627H179.071L179.188 176.618L179.305 176.605L179.402 176.591C179.415 176.589 179.428 176.587 179.442 176.583L179.531 176.566L179.554 176.561L179.653 176.54L179.688 176.531C179.721 176.523 179.757 176.513 179.792 176.503C179.827 176.493 179.875 176.48 179.917 176.466C179.999 176.441 180.082 176.414 180.166 176.383C180.25 176.353 180.345 176.316 180.434 176.278C180.524 176.241 180.613 176.203 180.696 176.161C180.741 176.141 180.786 176.12 180.828 176.098L180.962 176.032C181.135 175.943 181.309 175.848 181.483 175.745C181.622 175.664 181.76 175.579 181.898 175.491L182.031 175.401C182.076 175.373 182.121 175.344 182.164 175.312L182.297 175.223L182.368 175.174C182.432 175.129 182.497 175.084 182.56 175.039C182.739 174.916 182.906 174.789 183.075 174.66L183.09 174.648C183.18 174.579 183.269 174.51 183.359 174.44C183.726 174.15 184.074 173.858 184.39 173.583L184.6 173.399L184.619 173.381L184.729 173.284C184.987 173.052 185.217 172.836 185.408 172.658L185.487 172.581C185.556 172.516 185.619 172.455 185.676 172.403L185.788 172.292L185.828 172.253L185.839 172.242C185.886 172.194 185.928 172.152 185.956 172.125C185.997 172.081 186.022 172.056 186.03 172.048L186.039 172.041L186.074 172.009C186.086 171.997 186.101 171.984 186.118 171.969L186.132 171.956L186.169 171.922L186.373 171.743L186.487 171.641C186.542 171.593 186.604 171.538 186.666 171.479L186.802 171.358C186.827 171.338 186.851 171.316 186.876 171.294L187.019 171.169L187.229 170.984L187.341 170.887C187.776 170.509 188.305 170.052 188.913 169.537L189.162 169.326L189.573 168.981C189.711 168.866 189.851 168.749 189.994 168.63C190.544 168.173 191.136 167.688 191.762 167.185L192.173 166.855C192.523 166.576 192.882 166.292 193.246 166.006C193.393 165.891 193.542 165.776 193.694 165.662C193.917 165.489 194.142 165.315 194.371 165.141C194.517 165.028 194.666 164.916 194.817 164.803C195.675 164.155 196.56 163.506 197.456 162.874L197.84 162.606C198.109 162.421 198.377 162.235 198.645 162.054L198.888 161.89C199.142 161.719 199.395 161.552 199.647 161.386C199.88 161.234 200.111 161.085 200.343 160.939L200.586 160.786L200.827 160.636C201.069 160.486 201.309 160.339 201.548 160.196L201.787 160.053L202.265 159.775C202.422 159.684 202.579 159.594 202.734 159.506L202.829 159.454C202.953 159.385 203.077 159.317 203.2 159.25C203.355 159.166 203.509 159.085 203.663 159.006L203.892 158.888L204.115 158.776C204.193 158.739 204.27 158.7 204.346 158.663C204.793 158.443 205.24 158.243 205.666 158.068C205.737 158.039 205.809 158.009 205.88 157.979C206.021 157.919 206.161 157.865 206.3 157.818C206.439 157.77 206.576 157.722 206.71 157.674C206.833 157.633 206.953 157.594 207.068 157.559L207.108 157.547C207.17 157.527 207.232 157.509 207.293 157.493L207.311 157.488C207.439 157.451 207.566 157.419 207.691 157.389H207.7C207.887 157.345 208.07 157.306 208.248 157.277C208.308 157.266 208.368 157.256 208.427 157.248C208.546 157.23 208.662 157.216 208.777 157.206C208.835 157.2 208.891 157.195 208.948 157.193C209.061 157.185 209.171 157.181 209.279 157.181C209.307 157.181 209.335 157.181 209.363 157.181C209.475 157.181 209.583 157.188 209.69 157.199C209.739 157.199 209.788 157.209 209.836 157.215H209.856C209.904 157.221 209.952 157.228 210 157.239C210.047 157.248 210.095 157.256 210.141 157.267H210.156C210.203 157.277 210.245 157.289 210.294 157.303C210.342 157.316 210.388 157.329 210.434 157.346C210.593 157.4 210.746 157.469 210.891 157.552C210.933 157.576 210.973 157.601 211.012 157.628C211.051 157.653 211.09 157.68 211.128 157.709C211.193 157.758 211.257 157.811 211.317 157.868L211.344 157.894C211.362 157.91 211.379 157.927 211.395 157.944L211.444 157.997C211.846 158.418 212.178 158.901 212.428 159.427C212.441 159.457 212.454 159.487 212.466 159.517C212.491 159.576 212.515 159.634 212.536 159.696C212.588 159.839 212.632 159.986 212.666 160.135C212.796 160.726 212.76 161.342 212.564 161.914C212.547 161.967 212.528 162.018 212.508 162.07C212.435 162.265 212.349 162.454 212.251 162.637C212.225 162.686 212.198 162.736 212.169 162.785C211.858 163.309 211.489 163.796 211.07 164.237L210.981 164.332C210.848 164.472 210.71 164.612 210.565 164.752C210.501 164.815 210.434 164.877 210.367 164.94C210.3 165.003 210.231 165.065 210.162 165.129L210.055 165.224C209.984 165.287 209.912 165.35 209.839 165.413C209.692 165.54 209.54 165.667 209.383 165.796C209.342 165.828 209.302 165.86 209.263 165.893C209.104 166.022 208.939 166.151 208.77 166.281C208.472 166.51 208.162 166.74 207.841 166.973C207.563 167.173 207.275 167.374 206.979 167.578C206.621 167.823 206.251 168.071 205.869 168.321C205.189 168.767 204.471 169.224 203.715 169.693C203.507 169.823 203.297 169.953 203.083 170.084C201.115 171.294 198.934 172.588 196.609 173.995L196.007 174.36C195.348 174.762 194.726 175.146 194.14 175.512L193.845 175.697L193.287 176.055C193.067 176.196 192.852 176.334 192.643 176.469C192.485 176.57 192.331 176.67 192.179 176.77L191.882 176.966C191.734 177.062 191.59 177.158 191.449 177.252L191.308 177.342C191.161 177.44 191.017 177.537 190.876 177.633L190.647 177.79L190.379 177.976L190.13 178.149C189.713 178.444 189.325 178.725 188.968 178.992L188.834 179.094C188.683 179.208 188.537 179.322 188.396 179.433C188.333 179.482 188.272 179.531 188.211 179.58C187.902 179.829 187.62 180.067 187.367 180.296L187.243 180.409C187.172 180.474 187.102 180.539 187.035 180.603C186.989 180.648 186.946 180.693 186.898 180.736L186.834 180.8C186.792 180.841 186.751 180.883 186.711 180.924C186.606 181.033 186.508 181.139 186.416 181.242L186.35 181.318C186.203 181.488 186.075 181.651 185.963 181.81L185.913 181.881C185.825 182.009 185.745 182.14 185.671 182.277C185.652 182.311 185.635 182.345 185.618 182.379C185.601 182.413 185.585 182.447 185.569 182.481C185.554 182.515 185.547 182.53 185.536 182.555L185.515 182.605L185.498 182.65C185.49 182.67 185.482 182.69 185.475 182.711C185.467 182.732 185.454 182.768 185.445 182.8C185.413 182.898 185.387 182.998 185.367 183.099C185.362 183.121 185.358 183.145 185.355 183.167C185.351 183.189 185.347 183.212 185.345 183.234C185.342 183.257 185.339 183.276 185.337 183.296L185.331 183.354C185.331 183.378 185.331 183.401 185.331 183.425C185.331 183.448 185.331 183.448 185.331 183.459C185.331 183.486 185.331 183.512 185.331 183.539C185.331 183.566 185.331 183.607 185.331 183.641C185.331 183.65 185.331 183.659 185.331 183.669C185.331 183.695 185.331 183.721 185.338 183.749C185.339 183.765 185.341 183.781 185.343 183.797C185.343 183.823 185.349 183.848 185.353 183.876C185.357 183.902 185.364 183.949 185.372 183.986C185.372 183.986 185.372 183.986 185.372 183.991C185.379 184.026 185.386 184.06 185.395 184.095C185.404 184.13 185.413 184.17 185.424 184.206C185.443 184.277 185.467 184.347 185.492 184.417C185.508 184.459 185.523 184.5 185.54 184.541C185.54 184.549 185.546 184.558 185.55 184.566C185.561 184.592 185.573 184.62 185.586 184.647C185.598 184.674 185.619 184.721 185.636 184.758C185.671 184.832 185.709 184.907 185.75 184.982C185.77 185.019 185.791 185.057 185.813 185.094L185.879 185.208C185.901 185.246 185.924 185.284 185.947 185.322C185.96 185.341 185.973 185.359 185.988 185.376L186.01 185.399L186.035 185.422L186.061 185.442C186.08 185.455 186.099 185.467 186.119 185.477C186.139 185.487 186.16 185.496 186.183 185.505C186.206 185.513 186.23 185.519 186.254 185.525C186.831 185.655 188.017 185.178 189.593 184.346C189.682 184.298 189.78 184.248 189.875 184.196L190.355 183.934L190.589 183.804C190.756 183.715 190.926 183.614 191.1 183.515C191.205 183.456 191.31 183.396 191.417 183.336C193.5 182.137 195.988 180.597 198.56 179.093C198.801 178.952 199.043 178.811 199.285 178.672L199.771 178.361C200.013 178.222 200.256 178.084 200.498 177.947C200.822 177.764 201.147 177.583 201.471 177.404C202.199 177.004 202.925 176.618 203.639 176.254L204.115 176.013C204.431 175.857 204.744 175.705 205.053 175.557C205.363 175.41 205.67 175.269 205.974 175.134C206.276 175 206.574 174.871 206.868 174.748L207.203 174.612L207.243 174.596C209.018 173.893 210.627 173.459 211.929 173.459C212.211 173.456 212.492 173.48 212.769 173.528H212.778C212.867 173.544 212.948 173.562 213.031 173.582H213.046C213.114 173.599 213.181 173.619 213.246 173.641C213.298 173.658 213.348 173.675 213.397 173.694C213.446 173.713 213.474 173.724 213.512 173.741C213.563 173.764 213.613 173.787 213.662 173.812C213.711 173.837 213.778 173.874 213.833 173.908C214.042 174.034 214.23 174.19 214.393 174.371C214.465 174.451 214.531 174.536 214.591 174.625C214.612 174.654 214.631 174.683 214.649 174.714C214.687 174.773 214.722 174.833 214.756 174.893C214.799 174.971 214.838 175.052 214.875 175.137C214.911 175.223 214.945 175.31 214.978 175.406C215.01 175.501 215.038 175.594 215.067 175.693C215.278 176.45 215.257 177.253 215.007 177.998Z" fill="#FF9D00"/>
+<path fill-rule="evenodd" clip-rule="evenodd" d="M203.21 123.685V123.194C203.21 81.3404 169.292 47.4113 127.435 47.4113C85.5791 47.4113 51.6482 81.3424 51.6482 123.194C51.6482 123.249 51.6477 123.304 51.6472 123.358C51.6462 123.467 51.6452 123.576 51.6482 123.685C51.6511 123.789 51.6514 123.893 51.6517 123.997C51.6518 124.056 51.652 124.115 51.6526 124.174C51.654 124.293 51.6562 124.416 51.6584 124.534C51.6592 124.578 51.66 124.621 51.6607 124.663C51.6617 124.723 51.6634 124.782 51.6651 124.842C51.6678 124.937 51.6705 125.033 51.6705 125.128L51.6813 125.517C51.6858 125.669 51.6911 125.822 51.6965 125.974L51.7019 126.124C51.7079 126.282 51.7146 126.44 51.7215 126.597L51.7224 126.62C51.7303 126.805 51.7399 126.989 51.7494 127.173L51.7502 127.187C51.76 127.375 51.7698 127.564 51.7815 127.753C51.7913 127.927 51.8029 128.101 51.8145 128.275C51.8152 128.285 51.8162 128.296 51.8172 128.306C51.819 128.325 51.8208 128.345 51.8208 128.364C51.8288 128.495 51.8378 128.625 51.8476 128.754L51.8505 128.794C51.8612 128.938 51.8719 129.082 51.8834 129.226L51.8856 129.254C51.892 129.338 51.8985 129.422 51.9058 129.503C51.935 129.857 51.9666 130.211 52.0006 130.565C52.0369 130.932 52.0751 131.299 52.115 131.664L52.1204 131.709C52.1401 131.885 52.1589 132.053 52.1812 132.238C52.2036 132.423 52.2259 132.608 52.2492 132.793C52.265 132.919 52.2816 133.044 52.2982 133.17C52.306 133.229 52.3139 133.288 52.3216 133.347C52.3458 133.532 52.3717 133.717 52.3976 133.901C52.4173 134.041 52.4379 134.183 52.4585 134.323C52.4701 134.405 52.4826 134.487 52.4951 134.568L52.4992 134.595C52.5185 134.723 52.5377 134.852 52.5577 134.979C52.5631 134.992 52.5631 135.004 52.5631 135.016C52.6227 135.381 52.6823 135.742 52.742 136.1C52.8982 137.012 53.0708 137.918 53.2598 138.819C53.3748 139.359 53.4941 139.896 53.6175 140.429L53.6229 140.451C53.6335 140.496 53.6442 140.541 53.6548 140.586C53.685 140.715 53.7152 140.843 53.7463 140.971L53.8017 140.904C56.0018 138.274 59.1579 136.824 62.6887 136.824C65.5183 136.824 68.4222 137.76 71.3315 139.605C73.2623 140.831 75.3961 143.002 77.5918 145.733C79.6237 142.911 82.472 141.035 85.7299 140.523C86.3514 140.425 86.9792 140.376 87.608 140.375C95.0443 140.375 99.5233 146.828 101.215 152.633C102.051 154.594 106.08 163.526 112.156 169.568C121.392 178.795 123.703 188.316 119.132 198.511H119.148C119.459 198.546 119.772 198.578 120.087 198.607C120.274 198.625 120.46 198.643 120.648 198.659L120.714 198.665C120.851 198.677 120.989 198.689 121.127 198.7L121.507 198.73C121.869 198.758 122.232 198.784 122.596 198.807L122.885 198.824C122.961 198.829 123.037 198.833 123.114 198.838C123.161 198.84 123.209 198.843 123.256 198.846L123.703 198.869L123.825 198.874L124.294 198.895L124.816 198.915L125.235 198.927C125.259 198.928 125.282 198.928 125.305 198.929C125.394 198.933 125.483 198.936 125.572 198.936C125.604 198.937 125.636 198.938 125.668 198.939C125.767 198.942 125.868 198.945 125.968 198.945C126.457 198.955 126.946 198.959 127.437 198.959H128.063L128.51 198.954C128.62 198.949 128.729 198.949 128.84 198.949H129.014C129.064 198.948 129.114 198.947 129.165 198.945C129.224 198.943 129.283 198.941 129.343 198.941H129.522L129.873 198.932L130.401 198.914L130.982 198.888C131.15 198.882 131.316 198.873 131.482 198.865L131.661 198.854C131.75 198.85 131.838 198.845 131.927 198.84C131.979 198.837 132.031 198.833 132.083 198.831L132.201 198.823L132.738 198.788C132.916 198.776 133.095 198.762 133.274 198.749L133.761 198.71L134.103 198.681L134.479 198.647C134.864 198.611 135.247 198.573 135.63 198.533C135.873 198.507 136.115 198.479 136.359 198.45L136.786 198.399C132.287 188.247 134.616 178.767 143.813 169.577C149.876 163.519 153.905 154.587 154.745 152.625C156.438 146.821 160.914 140.368 168.352 140.368C168.981 140.368 169.61 140.418 170.231 140.516C173.486 141.028 176.334 142.904 178.369 145.726C180.566 142.996 182.699 140.823 184.63 139.597C187.539 137.753 190.445 136.817 193.272 136.817C196.388 136.817 199.212 137.947 201.345 140.02C201.384 139.851 201.422 139.681 201.459 139.512C201.496 139.343 201.532 139.174 201.568 139.006C201.607 138.821 201.646 138.636 201.683 138.451C201.749 138.124 201.815 137.797 201.878 137.467C201.944 137.125 202.007 136.781 202.067 136.437C202.078 136.375 202.088 136.313 202.098 136.251C202.117 136.141 202.135 136.031 202.156 135.92C202.19 135.748 202.218 135.576 202.246 135.402L202.257 135.336C202.281 135.185 202.305 135.034 202.328 134.883L202.398 134.424L202.398 134.42C202.427 134.227 202.455 134.034 202.482 133.841C202.503 133.696 202.523 133.549 202.542 133.403L202.553 133.319L202.616 132.841L202.667 132.433C202.697 132.166 202.727 131.898 202.757 131.629C202.769 131.521 202.781 131.413 202.792 131.306L202.801 131.218C202.82 131.044 202.838 130.87 202.854 130.696C202.854 130.692 202.854 130.687 202.854 130.682C202.867 130.544 202.881 130.405 202.893 130.266C202.964 129.478 203.024 128.686 203.072 127.891C203.081 127.761 203.088 127.63 203.096 127.499L203.096 127.493C203.105 127.329 203.114 127.166 203.122 127.002L203.128 126.892C203.135 126.738 203.142 126.582 203.148 126.427C203.155 126.252 203.162 126.075 203.169 125.896V125.884C203.17 125.841 203.172 125.798 203.174 125.754C203.179 125.645 203.183 125.535 203.183 125.425L203.185 125.381C203.189 125.278 203.193 125.172 203.193 125.067C203.194 125.037 203.195 125.007 203.196 124.977C203.199 124.872 203.202 124.768 203.202 124.663C203.203 124.634 203.203 124.604 203.204 124.574C203.207 124.441 203.21 124.307 203.21 124.174V123.685ZM108.638 199.391C114.64 190.59 114.214 183.984 105.98 175.754C97.7443 167.523 92.9511 155.487 92.9511 155.487C92.9511 155.487 91.1624 148.496 87.0822 149.138C83.0025 149.78 80.0092 160.227 88.5519 166.622C97.0941 173.017 86.8527 177.353 83.5642 171.352C80.2757 165.35 71.2992 149.923 66.6452 146.972C61.991 144.021 58.7178 145.675 59.8152 151.757C60.3598 154.776 65.4278 159.929 70.1627 164.743C74.9667 169.627 79.4279 174.163 78.4744 175.768C76.5812 178.955 69.9139 172.023 69.9139 172.023C69.9139 172.023 49.0385 153.025 44.4936 157.976C40.3048 162.539 46.7652 166.418 56.7205 172.397C57.5673 172.905 58.4393 173.429 59.3322 173.969C70.7233 180.865 71.6087 182.684 69.9926 185.293C69.3952 186.257 65.5819 183.968 60.8927 181.153C52.8972 176.352 42.3549 170.023 40.8662 175.688C39.5777 180.591 47.3335 183.595 54.3678 186.32C60.228 188.59 65.5873 190.666 64.7992 193.484C63.9823 196.406 59.5533 193.969 54.7118 191.305C49.2766 188.314 43.3215 185.038 41.3732 188.735C37.6895 195.725 66.7838 203.954 67.0225 204.015C76.4226 206.453 100.295 211.619 108.638 199.391ZM147.303 199.391C141.301 190.59 141.727 183.984 149.962 175.754C158.197 167.523 162.99 155.487 162.99 155.487C162.99 155.487 164.779 148.496 168.859 149.138C172.939 149.78 175.932 160.227 167.39 166.622C158.847 173.017 169.089 177.353 172.377 171.352C175.666 165.35 184.637 149.923 189.291 146.972C193.945 144.021 197.22 145.675 196.122 151.757C195.578 154.776 190.509 159.929 185.774 164.744L185.774 164.744C180.97 169.628 176.509 174.163 177.462 175.768C179.355 178.955 186.027 172.019 186.027 172.019C186.027 172.019 206.902 153.022 211.448 157.973C215.637 162.535 209.176 166.415 199.219 172.394C198.373 172.902 197.502 173.426 196.609 173.966C185.218 180.862 184.332 182.681 185.948 185.289C186.546 186.254 190.359 183.964 195.048 181.149C203.044 176.349 213.586 170.019 215.075 175.685C216.364 180.588 208.607 183.592 201.573 186.317C195.713 188.587 190.353 190.663 191.141 193.481C191.957 196.402 196.385 193.965 201.225 191.301C206.66 188.31 212.616 185.032 214.564 188.732C218.248 195.726 189.15 203.947 188.915 204.007C179.515 206.453 155.643 211.619 147.303 199.391Z" fill="#FFD21E"/>
+<path fill-rule="evenodd" clip-rule="evenodd" d="M152.047 102.567C153.229 102.985 154.108 104.257 154.944 105.468C156.074 107.104 157.126 108.627 158.74 107.769C160.644 106.756 162.205 105.202 163.226 103.302C164.247 101.402 164.681 99.2425 164.475 97.0959C164.268 94.9493 163.43 92.9121 162.065 91.2421C160.7 89.5721 158.871 88.3441 156.809 87.7134C154.746 87.0826 152.543 87.0778 150.478 87.6993C148.413 88.3208 146.578 89.5405 145.206 91.2047C143.834 92.8688 142.987 94.9021 142.771 97.0478C142.555 99.1939 142.98 101.356 143.992 103.26C144.74 104.667 146.4 104.003 148.152 103.302C149.525 102.753 150.956 102.181 152.047 102.567ZM100.672 102.567C99.4902 102.985 98.611 104.258 97.7748 105.468C96.6443 107.105 95.5925 108.627 93.9786 107.769C92.0747 106.757 90.5136 105.202 89.4933 103.302C88.4725 101.402 88.0382 99.243 88.2449 97.0964C88.4521 94.9498 89.2905 92.9126 90.6551 91.2426C92.0197 89.5726 93.8488 88.3451 95.9112 87.7144C97.9737 87.0841 100.177 87.0792 102.242 87.7008C104.307 88.3223 106.141 89.542 107.513 91.2056C108.885 92.8698 109.733 94.9031 109.949 97.0488C110.165 99.1944 109.74 101.356 108.728 103.26C107.979 104.667 106.319 104.003 104.567 103.303C103.193 102.753 101.764 102.181 100.672 102.567ZM144.099 149.318C152.242 142.903 155.233 132.429 155.233 125.977C155.233 120.877 151.802 122.482 146.309 125.202C146.206 125.252 146.103 125.304 145.999 125.355C140.957 127.852 134.245 131.177 126.877 131.177C119.508 131.177 112.796 127.852 107.755 125.354C102.084 122.545 98.5266 120.783 98.5266 125.978C98.5266 132.634 101.709 143.563 110.443 149.912C111.596 147.572 113.219 145.496 115.21 143.812C117.202 142.129 119.519 140.873 122.018 140.126C122.89 139.866 123.788 141.367 124.707 142.904C125.594 144.386 126.501 145.902 127.423 145.902C128.406 145.902 129.371 144.408 130.314 142.95C131.299 141.425 132.26 139.94 133.189 140.237C133.46 140.323 133.728 140.416 133.994 140.514C138.322 142.121 141.914 145.252 144.099 149.318Z" fill="#32343D"/>
+<path d="M144.097 149.317C139.856 152.659 134.219 154.9 126.878 154.9C119.981 154.9 114.587 152.922 110.443 149.911C111.596 147.572 113.219 145.495 115.21 143.812C117.202 142.128 119.519 140.873 122.018 140.125C123.73 139.614 125.545 145.901 127.423 145.901C129.433 145.901 131.37 139.655 133.189 140.236C133.459 140.322 133.727 140.414 133.994 140.514C138.32 142.121 141.912 145.252 144.097 149.317Z" fill="#FF323D"/>
+<path fill-rule="evenodd" clip-rule="evenodd" d="M81.2008 111.64C80.0351 112.42 78.6646 112.835 77.2626 112.835C76.3318 112.835 75.4097 112.652 74.5494 112.295C73.6891 111.94 72.9078 111.417 72.2495 110.759C71.5912 110.1 71.0692 109.319 70.7131 108.458C70.357 107.598 70.1739 106.676 70.1743 105.745C70.1746 104.343 70.5906 102.973 71.3697 101.808C72.1489 100.642 73.2561 99.7344 74.5514 99.198C75.8464 98.6616 77.2718 98.5215 78.6466 98.7953C80.0215 99.0691 81.2844 99.7441 82.2756 100.736C83.2667 101.727 83.9417 102.99 84.215 104.365C84.4883 105.74 84.3478 107.165 83.8113 108.46C83.2749 109.755 82.3665 110.862 81.2008 111.64ZM182.613 111.64C181.447 112.42 180.076 112.835 178.675 112.835C177.744 112.835 176.822 112.652 175.962 112.295C175.101 111.94 174.32 111.417 173.661 110.759C173.003 110.1 172.481 109.319 172.125 108.458C171.769 107.598 171.586 106.676 171.586 105.745C171.587 104.343 172.003 102.973 172.782 101.808C173.561 100.642 174.668 99.7344 175.963 99.198C177.259 98.6616 178.684 98.5215 180.059 98.7953C181.434 99.0691 182.697 99.7441 183.688 100.736C184.679 101.727 185.354 102.99 185.627 104.365C185.901 105.74 185.76 107.165 185.224 108.46C184.687 109.755 183.778 110.862 182.613 111.64Z" fill="#FFAD03"/>
+<defs>
+<filter id="filter0_d_3_42" x="12.4697" y="22.1456" width="231.956" height="217.2" filterUnits="userSpaceOnUse" color-interpolation-filters="sRGB">
+<feFlood flood-opacity="0" result="BackgroundImageFix"/>
+<feColorMatrix in="SourceAlpha" type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 0" result="hardAlpha"/>
+<feOffset dy="3"/>
+<feGaussianBlur stdDeviation="6.5"/>
+<feColorMatrix type="matrix" values="0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0"/>
+<feBlend mode="normal" in2="BackgroundImageFix" result="effect1_dropShadow_3_42"/>
+<feBlend mode="normal" in="SourceGraphic" in2="effect1_dropShadow_3_42" result="shape"/>
+</filter>
+</defs>
+</svg>

docs/installation.md

 # Installation
 
-We recommend **Python 3.8** or higher, **[PyTorch 1.11.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.32.0](https://github.com/huggingface/transformers)** or higher.
+We recommend **Python 3.8+**, **[PyTorch 1.11.0+](https://pytorch.org/get-started/locally/)**, and **[transformers v4.34.0+](https://github.com/huggingface/transformers)**. There are three options to install Sentence Transformers:
+* **Default:** This allows for loading, saving, and inference (i.e., getting embeddings) of models.
+* **Default and Training**: All of the above plus training.
+* **Development**: All of the above plus some dependencies for developing Sentence Transformers, see [Editable Install](#editable-install).
 
-## Install SentenceTransformers
+## Install with pip
 
-**Install with pip**
+```eval_rst
+
+.. tab:: Default
+
+    ::
+
+        pip install -U sentence-transformers
+
+.. tab:: Default and Training
+
+    ::
+
+        pip install -U "sentence-transformers[train]"
+
+    To use `Weights and Biases <https://wandb.ai/>`_ to track your training logs, you should also install ``wandb`` **(recommended)**::
+
+        pip install wandb
+    
+    And to track your Carbon Emissions while training and have this information automatically included in your model cards, also install ``codecarbon`` **(recommended)**::
+
+        pip install codecarbon
+
+.. tab:: Development
+
+    ::
+
+        pip install -U "sentence-transformers[dev]"
 
-Install the *sentence-transformers* with `pip`:
-```
-pip install -U sentence-transformers
 ```
 
-**Install with conda**
+## Install with Conda
+
+```eval_rst
+
+.. tab:: Default
+
+    ::
+
+        conda install -c conda-forge sentence-transformers
+
+.. tab:: Default and Training
+
+    ::
+
+        conda install -c conda-forge sentence-transformers accelerate datasets
+
+    To use `Weights and Biases <https://wandb.ai/>`_ to track your training logs, you should also install ``wandb`` **(recommended)**::
+
+        pip install wandb
+    
+    And to track your Carbon Emissions while training and have this information automatically included in your model cards, also install ``codecarbon`` **(recommended)**::
+
+        pip install codecarbon
+
+.. tab:: Development
+
+    ::
+
+        conda install -c conda-forge sentence-transformers accelerate datasets pre-commit pytest ruff
 
-Apple silicon Installation of *sentence-transformers*
 ```
-conda install -c conda-forge sentence-transformers
+
+## Install from Source
+
+You can install ``sentence-transformers`` directly from source to take advantage of the bleeding edge `master` branch rather than the latest stable release:
+
+```eval_rst
+
+.. tab:: Default
+
+    ::
+
+        pip install git+https://github.com/UKPLab/sentence-transformers.git
+
+.. tab:: Default and Training
+
+    ::
+
+        pip install -U "sentence-transformers[train] @ git+https://github.com/UKPLab/sentence-transformers.git"
+
+    To use `Weights and Biases <https://wandb.ai/>`_ to track your training logs, you should also install ``wandb`` **(recommended)**::
+
+        pip install wandb
+    
+    And to track your carbon emissions while training and have this information automatically included in your model cards, also install ``codecarbon`` **(recommended)**::
+
+        pip install codecarbon
+
+.. tab:: Development
+
+    ::
+
+        pip install -U "sentence-transformers[dev] @ git+https://github.com/UKPLab/sentence-transformers.git"
+
 ```
 
-**Install from source**
+## Editable Install
+
+If you want to make changes to ``sentence-transformers``, you will need an editable install. Clone the repository and install it with these commands:
+```
+git clone https://github.com/UKPLab/sentence-transformers
+cd sentence-transformers
+pip install -e ".[train,dev]"
+```
 
-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 .
-```` 
+These commands will link the new `sentence-transformers` folder and your Python library paths, such that this folder will be used when importing `sentence-transformers`.
 
 ## Install PyTorch with CUDA support
 
-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.
+To use a GPU/CUDA, you must install PyTorch with CUDA support. Follow [PyTorch - Get Started](https://pytorch.org/get-started/locally/) for installation steps.
\ No newline at end of file

docs/package_reference/cross_encoder/cross_encoder.md

+# CrossEncoder
+
+## CrossEncoder
+For an introduction to Cross-Encoders, see [Cross-Encoders](../../examples/applications/cross-encoder/README.md).
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.CrossEncoder
+   :members:
+```
+
+## Training Inputs
+
+```eval_rst
+.. autoclass:: sentence_transformers.readers.InputExample
+```
\ No newline at end of file

docs/package_reference/cross_encoder/evaluation.md

+# Evaluation
+CrossEncoder have their own evaluation classes, that are in `sentence_transformers.cross_encoder.evaluation`.
+
+## CEBinaryAccuracyEvaluator
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.evaluation.CEBinaryAccuracyEvaluator
+```
+## CEBinaryClassificationEvaluator
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.evaluation.CEBinaryClassificationEvaluator
+```
+
+## CECorrelationEvaluator
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.evaluation.CECorrelationEvaluator
+```
+
+## CEF1Evaluator
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.evaluation.CEF1Evaluator
+```
+
+## CESoftmaxAccuracyEvaluator
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.evaluation.CESoftmaxAccuracyEvaluator
+```
+
+## CERerankingEvaluator
+```eval_rst
+.. autoclass:: sentence_transformers.cross_encoder.evaluation.CERerankingEvaluator
+```
\ No newline at end of file