Commit ab306518 authored by LysandreJik's avatar LysandreJik
Browse files

Hugging Face theme.

parent a60ae1a5
......@@ -1116,22 +1116,22 @@ An overview of the implemented schedules:
- `ConstantLR`: always returns learning rate 1.
- `WarmupConstantSchedule`: Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
Keeps learning rate equal to 1. after warmup.
![](docs/imgs/warmup_constant_schedule.png)
![](docs/source/imgs/warmup_constant_schedule.png)
- `WarmupLinearSchedule`: Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
![](docs/imgs/warmup_linear_schedule.png)
![](docs/source/imgs/warmup_linear_schedule.png)
- `WarmupCosineSchedule`: Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
![](docs/imgs/warmup_cosine_schedule.png)
![](docs/source/imgs/warmup_cosine_schedule.png)
- `WarmupCosineWithHardRestartsSchedule`: Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying learning rate (with hard restarts).
![](docs/imgs/warmup_cosine_hard_restarts_schedule.png)
![](docs/source/imgs/warmup_cosine_hard_restarts_schedule.png)
- `WarmupCosineWithWarmupRestartsSchedule`: All training progress is divided in `cycles` (default=1.) parts of equal length.
Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
followed by a learning rate decreasing from 1. to 0. following a cosine curve.
Note that the total number of all warmup steps over all cycles together is equal to `warmup` * `cycles`
![](docs/imgs/warmup_cosine_warm_restarts_schedule.png)
![](docs/source/imgs/warmup_cosine_warm_restarts_schedule.png)
## Examples
......
.highlight .c1{
color: #999
}
.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp {
color: #FB8D68;
}
.highlight .kn, .highlight .nv, .highlight .s2 {
color: #6670FF;
}
\ No newline at end of file
/* The literal code blocks */
.rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
color: #6670FF;
}
/* To keep the logo centered */
.wy-side-scroll {
width: auto;
}
/* The div that holds the Hugging Face logo */
.HuggingFaceDiv {
width: 100%
}
/* The research field on top of the toc tree */
.wy-side-nav-search{
background-color: #6670FF;
}
/* The toc tree */
.wy-nav-side{
background-color: #6670FF;
}
/* The selected items in the toc tree */
.wy-menu-vertical li.current{
background-color: #A6B0FF;
}
/* When a list item that does belong to the selected block from the toc tree is hovered */
.wy-menu-vertical li.current a:hover{
background-color: #FB8D68;
}
/* When a list item that does NOT belong to the selected block from the toc tree is hovered. */
.wy-menu-vertical li a:hover{
background-color: #FB8D68;
}
/* The text items on the toc tree */
.wy-menu-vertical a {
color: #FFFFDD;
font-family: Calibre-Light;
}
.wy-menu-vertical header, .wy-menu-vertical p.caption{
color: white;
font-family: Calibre-Light;
}
/* The color inside the selected toc tree block */
.wy-menu-vertical li.toctree-l2 a, .wy-menu-vertical li.toctree-l3 a, .wy-menu-vertical li.toctree-l4 a {
color: black;
}
/* Inside the depth-2 selected toc tree block */
.wy-menu-vertical li.toctree-l2.current>a {
background-color: #B6C0FF
}
.wy-menu-vertical li.toctree-l2.current li.toctree-l3>a {
background-color: #C6D0FF
}
/* Inside the depth-3 selected toc tree block */
.wy-menu-vertical li.toctree-l3.current li.toctree-l4>a{
background-color: #D6E0FF
}
/* Inside code snippets */
.rst-content dl:not(.docutils) dt{
font-size: 15px;
}
/* Links */
a {
color: #6670FF;
}
/* Content bars */
.rst-content dl:not(.docutils) dt {
background-color: rgba(251, 141, 104, 0.1);
border-right: solid 2px #FB8D68;
border-left: solid 2px #FB8D68;
color: #FB8D68;
font-family: Calibre-Light;
border-top: none;
font-style: normal !important;
}
/* Expand button */
.wy-menu-vertical li.toctree-l2 span.toctree-expand,
.wy-menu-vertical li.on a span.toctree-expand, .wy-menu-vertical li.current>a span.toctree-expand,
.wy-menu-vertical li.toctree-l3 span.toctree-expand{
color: black;
}
/* Max window size */
.wy-nav-content{
max-width: 1200px;
}
/* Mobile header */
.wy-nav-top{
background-color: #6670FF;
}
/* FONTS */
body{
font-family: Calibre;
font-size: 20px;
}
h1 {
font-family: Calibre-Thin;
font-size: 70px;
}
h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend{
font-family: Calibre-Medium;
}
@font-face {
font-family: Calibre-Medium;
src: url(./Calibre-Medium.otf);
font-weight:400;
}
@font-face {
font-family: Calibre;
src: url(./Calibre-Regular.otf);
font-weight:400;
}
@font-face {
font-family: Calibre-Light;
src: url(./Calibre-Light.ttf);
font-weight:400;
}
@font-face {
font-family: Calibre-Thin;
src: url(./Calibre-Thin.otf);
font-weight:400;
}
function addIcon() {
const huggingFaceLogo = "http://lysand.re/huggingface_logo.svg";
const image = document.createElement("img");
image.setAttribute("src", huggingFaceLogo)
const div = document.createElement("div")
div.appendChild(image);
div.style.textAlign = 'center';
div.style.paddingTop = '30px';
div.style.backgroundColor = '#6670FF'
const scrollDiv = document.getElementsByClassName("wy-side-scroll")[0];
scrollDiv.prepend(div)
}
window.addEventListener("load", addIcon)
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<title>icon</title>
<desc>Created with Sketch.</desc>
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......@@ -176,5 +176,9 @@ epub_title = project
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
def setup(app):
app.add_stylesheet('css/huggingface.css')
app.add_stylesheet('css/code-snippets.css')
app.add_js_file('js/custom.js')
# -- Extension configuration -------------------------------------------------
Pytorch-Transformers: The Big & Extending Repository of pretrained Transformers
Pytorch-Transformers
================================================================================================================================================
......
......@@ -39,10 +39,8 @@ configuration files. The respective configuration classes are:
These configuration classes contains a few utilities to load and save configurations:
* ``from_dict(cls, json_object)``\ : A class method to construct a configuration from a Python dictionary of parameters.
Returns an instance of the configuration class.
* ``from_json_file(cls, json_file)``\ : A class method to construct a configuration from a json file of parameters.
Returns an instance of the configuration class.
* ``from_dict(cls, json_object)``\ : A class method to construct a configuration from a Python dictionary of parameters. Returns an instance of the configuration class.
* ``from_json_file(cls, json_file)``\ : A class method to construct a configuration from a json file of parameters. Returns an instance of the configuration class.
* ``to_dict()``\ : Serializes an instance to a Python dictionary. Returns a dictionary.
* ``to_json_string()``\ : Serializes an instance to a JSON string. Returns a string.
* ``to_json_file(json_file_path)``\ : Save an instance to a json file.
......@@ -247,40 +245,44 @@ An overview of the implemented schedules:
* ``ConstantLR``\ : always returns learning rate 1.
* ``WarmupConstantSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
* ``WarmupConstantSchedule`` : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
Keeps learning rate equal to 1. after warmup.
.. image:: docs/imgs/warmup_constant_schedule.png
:target: docs/imgs/warmup_constant_schedule.png
.. image:: /imgs/warmup_constant_schedule.png
:target: /imgs/warmup_constant_schedule.png
:alt:
* ``WarmupLinearSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
* ``WarmupLinearSchedule`` : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
Linearly decreases learning rate from 1. to 0. over remaining ``1 - warmup`` steps.
.. image:: docs/imgs/warmup_linear_schedule.png
:target: docs/imgs/warmup_linear_schedule.png
.. image:: /imgs/warmup_linear_schedule.png
:target: /imgs/warmup_linear_schedule.png
:alt:
* ``WarmupCosineSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
* ``WarmupCosineSchedule`` : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
Decreases learning rate from 1. to 0. over remaining ``1 - warmup`` steps following a cosine curve.
If ``cycles`` (default=0.5) is different from default, learning rate follows cosine function after warmup.
.. image:: docs/imgs/warmup_cosine_schedule.png
:target: docs/imgs/warmup_cosine_schedule.png
.. image:: /imgs/warmup_cosine_schedule.png
:target: /imgs/warmup_cosine_schedule.png
:alt:
* ``WarmupCosineWithHardRestartsSchedule``\ : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
* ``WarmupCosineWithHardRestartsSchedule`` : Linearly increases learning rate from 0 to 1 over ``warmup`` fraction of training steps.
If ``cycles`` (default=1.) is different from default, learning rate follows ``cycles`` times a cosine decaying learning rate (with hard restarts).
.. image:: docs/imgs/warmup_cosine_hard_restarts_schedule.png
:target: docs/imgs/warmup_cosine_hard_restarts_schedule.png
.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
:target: /imgs/warmup_cosine_hard_restarts_schedule.png
:alt:
* ``WarmupCosineWithWarmupRestartsSchedule``\ : All training progress is divided in ``cycles`` (default=1.) parts of equal length.
* ``WarmupCosineWithWarmupRestartsSchedule`` : All training progress is divided in ``cycles`` (default=1.) parts of equal length.
Every part follows a schedule with the first ``warmup`` fraction of the training steps linearly increasing from 0. to 1.,
followed by a learning rate decreasing from 1. to 0. following a cosine curve.
Note that the total number of all warmup steps over all cycles together is equal to ``warmup`` * ``cycles``
.. image:: docs/imgs/warmup_cosine_warm_restarts_schedule.png
:target: docs/imgs/warmup_cosine_warm_restarts_schedule.png
.. image:: /imgs/warmup_cosine_warm_restarts_schedule.png
:target: /imgs/warmup_cosine_warm_restarts_schedule.png
:alt:
\ No newline at end of file
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