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name: CI | ||
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on: | ||
push: | ||
branches: | ||
- main | ||
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permissions: | ||
contents: read | ||
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jobs: | ||
# Build the documentation and upload the static HTML files as an artifact. | ||
build: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: actions/checkout@v4 | ||
- uses: actions/setup-python@v5 | ||
with: | ||
python-version: '3.12' | ||
- name: Install dependencies | ||
run: pip3 install -r requirements.txt | ||
- name: Build website | ||
run: python docs/make.py | ||
- uses: actions/upload-pages-artifact@v3 | ||
with: | ||
path: docs/ | ||
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deploy: | ||
needs: build | ||
runs-on: ubuntu-latest | ||
permissions: | ||
pages: write | ||
id-token: write | ||
environment: | ||
name: github-pages | ||
url: ${{ steps.deployment.outputs.page_url }} | ||
steps: | ||
- id: deployment | ||
uses: actions/deploy-pages@v4 |
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index.html |
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jonhue.github.io |
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## Build Instructions | ||
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Run `make.py` to build `index.html` and `./docs`. |
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from afsl import ActiveDataLoader, GradientEmebeddings | ||
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emb = GradientEmebeddings() | ||
train_loader = ActiveDataLoader(emb, dataset, task, batch_size=32) | ||
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while not converged: | ||
data = train_loader.next(model) | ||
model.step(data) |
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from afsl import ActiveDataLoader, GradientEmebeddings | ||
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emb = GradientEmebeddings() | ||
context_loader = ActiveDataLoader(emb, dataset, task, batch_size=5) | ||
data = context_loader.next(model) | ||
model.add_to_context(data) |
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<!doctype html> | ||
<html lang="en"> | ||
<head> | ||
<meta charset="utf-8"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1"> | ||
<meta name="description" content="afsl retrieves data intelligently to maximize the information gain about a downstream task. This can be used, for example, to select data for efficient few-shot fine-tuning or to populate a context for in-context learning."> | ||
<meta property="og:title" content="Active Few-Shot Learning"> | ||
<meta property="og:type" content="website"> | ||
<meta property="og:url" content="https://jonhue.github.io/afsl"> | ||
<title>Active Few-Shot Learning</title> | ||
{% autoescape false %}{% filter replace("\n","") | replace(" ","") | replace("<s","\n <s") %} | ||
<script type="application/ld+json"> | ||
{ | ||
"@context": "https://schema.org", | ||
"@type": "SoftwareApplication", | ||
"name": "afsl", | ||
"operatingSystem": "Python 3", | ||
"applicationCategory": "DeveloperApplication", | ||
"sameAs": [ | ||
"https://github.com/jonhue/afsl", | ||
"https://pypi.org/project/afsl/" | ||
], | ||
"offers": { | ||
"@type": "Offer", | ||
"price": "0", | ||
"priceCurrency": "USD" | ||
}, | ||
"review": { | ||
"@type": "Review", | ||
"reviewRating": { | ||
"@type": "Rating", | ||
"ratingValue": "5" | ||
}, | ||
"author": { | ||
"@type": "Person", | ||
"name": "Maximilian Hils", | ||
"sameAs": "https://hi.ls/" | ||
}, | ||
"reviewBody": "A lightweight alternative to Sphinx!" | ||
}, | ||
"url": "https://jonhue.github.io/afsl" | ||
} | ||
</script> | ||
<style>{% include 'resources/bootstrap.min.css' %}</style> | ||
<style>/* pygments */{{ pygments_css }}</style> | ||
<style> | ||
:root { | ||
--color1: #2b50aa; | ||
--color2: #F2545B; | ||
--color3: #E8C547; | ||
--color4: #419D78; | ||
} | ||
body { | ||
font-size: 1.25rem; | ||
} | ||
h1 { | ||
font-size: 2.75rem; | ||
} | ||
{% set boxwidth = 800 %} | ||
{% set boxpad = 20 %} | ||
{% set minw = boxwidth * 2 + boxpad * 4 %} | ||
@media (min-width: {{ minw }}px) { | ||
#example > input, #example > label { | ||
display: none; | ||
} | ||
} | ||
@media (max-width: {{ minw - 1 }}px) { | ||
#example-code, #example-render { | ||
display: none; | ||
} | ||
#view-code:checked ~ #example-code { | ||
display: block; | ||
} | ||
#view-render:checked ~ #example-render { | ||
display: block; | ||
} | ||
} | ||
#example > label { | ||
margin-bottom: 1rem; | ||
} | ||
#example > label[for=view-code] { | ||
border-top-right-radius: 0; | ||
border-bottom-right-radius: 0; | ||
} | ||
#example > label[for=view-render] { | ||
border-top-left-radius: 0; | ||
border-bottom-left-radius: 0; | ||
} | ||
#example-invocation code { | ||
font-size: 1.2em; | ||
display: inline-block; | ||
border: solid var(--bs-dark) 1px; | ||
background-color: rgba(255, 255, 255, 0.8); | ||
color: var(--bs-gray-dark); | ||
border-radius: 3px; | ||
padding: 3px 7px; | ||
margin-bottom: 32px; | ||
} | ||
#example-render, #example-code { | ||
width: {{ boxwidth }}px; | ||
max-width: 98vw; | ||
margin: 0 {{ boxpad }}px; | ||
flex: 0 0 auto; | ||
box-shadow: rgba(0, 0, 0, 0.2) 0 20px 68px; | ||
border-radius: 5px; | ||
} | ||
.example-code { | ||
background-color: var(--bs-dark) !important; | ||
position: relative; | ||
overflow: auto; | ||
} | ||
.example-code svg { | ||
position: absolute; | ||
left: 20px; | ||
top: 20px; | ||
} | ||
.example-code .title { | ||
color: white; | ||
min-height: 1em; | ||
margin-top: 20px; | ||
text-align: center; | ||
} | ||
.example-code .highlight { | ||
margin: 20px; | ||
margin-bottom: 30px; | ||
} | ||
.example-code pre { | ||
overflow: inherit; | ||
} | ||
#install { | ||
text-overflow: ellipsis; | ||
} | ||
#actions { | ||
& > :nth-child(1) { | ||
background-color: var(--color1); | ||
border-color: var(--color1); | ||
} | ||
& > :nth-child(2) { | ||
background-color: var(--color4); | ||
border-color: var(--color4); | ||
} | ||
& > :nth-child(3) { | ||
background-color: var(--color2); | ||
border-color: var(--color2); | ||
} | ||
} | ||
.highlighted { | ||
color: rgb(119, 148, 244); | ||
font-style: italic; | ||
} | ||
</style> | ||
{% endfilter %}{% endautoescape %} | ||
</head> | ||
{%- macro icon(name) -%} | ||
{% autoescape false %} | ||
{% filter replace('<svg ', '<svg aria-hidden="true" ') %} | ||
{% include "resources/" + name + ".svg" %} | ||
{% endfilter %} | ||
{% endautoescape %} | ||
{%- endmacro %} | ||
<body class="my-4"> | ||
<header class="container text-center my-4 py-4"> | ||
<div class="row justify-content-center mb-1 mt-4"> | ||
<h1>Active Few-Shot Learning</h1> | ||
</div> | ||
<div class="row justify-content-center mb-2"> | ||
<div class="col-md-7"> | ||
<p> | ||
Efficient fine-tuning & in-context learning by intelligent active data selection. | ||
</p> | ||
</div> | ||
</div> | ||
</header> | ||
<aside id="example" class="d-flex align-items-stretch justify-content-center flex-wrap"> | ||
<input type="radio" class="btn-check" name="view-selector" id="view-code" autocomplete="off" checked> | ||
<label class="btn btn-outline-dark" for="view-code">Fine-tuning</label> | ||
<input type="radio" class="btn-check" name="view-selector" id="view-render" autocomplete="off"> | ||
<label class="btn btn-outline-dark" for="view-render">In-context learning</label> | ||
<div class="col-12"></div> | ||
<div class="example-code" id="example-code"> | ||
<svg aria-hidden="true" xmlns="http://www.w3.org/2000/svg" width="54" height="14" viewBox="0 0 54 14"> | ||
<g> | ||
<circle cx="6" cy="6" r="6" fill="#FF5F56" stroke="#E0443E" stroke-width=".5"></circle> | ||
<circle cx="26" cy="6" r="6" fill="#FFBD2E" stroke="#DEA123" stroke-width=".5"></circle> | ||
<circle cx="46" cy="6" r="6" fill="#27C93F" stroke="#1AAB29" stroke-width=".5"></circle> | ||
</g> | ||
</svg> | ||
<div class="title"></div> | ||
{{ example_html1 }} | ||
</div> | ||
<div class="example-code" id="example-render"> | ||
<svg aria-hidden="true" xmlns="http://www.w3.org/2000/svg" width="54" height="14" viewBox="0 0 54 14"> | ||
<g> | ||
<circle cx="6" cy="6" r="6" fill="#FF5F56" stroke="#E0443E" stroke-width=".5"></circle> | ||
<circle cx="26" cy="6" r="6" fill="#FFBD2E" stroke="#DEA123" stroke-width=".5"></circle> | ||
<circle cx="46" cy="6" r="6" fill="#27C93F" stroke="#1AAB29" stroke-width=".5"></circle> | ||
</g> | ||
</svg> | ||
<div class="title"></div> | ||
{{ example_html2 }} | ||
</div> | ||
</aside> | ||
<main class="container text-center my-4 py-4"> | ||
<div class="container text-center" id="actions"> | ||
<a href="https://github.com/jonhue/active-few-shot-learning" | ||
itemprop="sameAs" | ||
class="btn btn-lg btn-dark shadow mb-4 d-inline-flex align-items-center"> | ||
{{ icon("github") }} | ||
GitHub | ||
</a> | ||
<a href="docs/afsl.html" class="btn btn-lg btn-dark shadow mb-4 d-inline-flex align-items-center"> | ||
{{ icon("book-half") }} | ||
Documentation | ||
</a> | ||
<a href="https://github.com/jonhue/afsl/blob/main/CHANGELOG.md" | ||
class="btn btn-lg btn-dark shadow mb-4 d-inline-flex align-items-center"> | ||
{{ icon("newspaper") }} | ||
Paper | ||
</a> | ||
<div class="d-md-none"></div> | ||
{# <a href="https://pypi.org/project/afsl/" | ||
itemprop="sameAs" | ||
class="btn btn-lg btn-warning shadow mb-4 d-inline-flex align-items-center"> | ||
{{ icon("box-seam") }} | ||
PyPI | ||
</a> #} | ||
</div> | ||
<div class="row justify-content-center mb-4"> | ||
<div class="col-md-7" style="max-width: 40rem; text-align: justify; hyphens: auto;"> | ||
<p> | ||
<code>afsl</code> retrieves data intelligently to maximize the information gain about a downstream task. | ||
This can be used, for example, to select data for efficient few-shot <i>fine-tuning</i> or to populate a context for <i>in-context learning</i>. | ||
To learn more about how <code>afsl</code> works, check out our <a href="https://yas.pub">paper</a> or our <a href="https://yas.pub">blog post</a>. | ||
</p> | ||
</div> | ||
</div> | ||
{# <div class="container text-center my-4"> | ||
<h3>Installation</h3> | ||
<div class="d-inline-block" style="max-width: 25rem; width: 100%;"> | ||
<div class="input-group input-group-lg shadow" onclick="getElementById('install').select()"> | ||
<label for="install" class="input-group-text" aria-label="install instructions"> | ||
{{ icon("terminal-fill") }} | ||
</label> | ||
<input id="install" type="text" class="form-control bg-white" value="pip install active-few-shot-learning" readonly> | ||
</div> | ||
</div> | ||
<div class="mb-3 text-muted">Latest Release: {{ __version__ }}</div> | ||
</div> #} | ||
<div class="container justify-content-center my-4" style="max-width: 40rem; text-align: justify; hyphens: auto;"> | ||
<h3>Publications</h3> | ||
{# <div class="list-group"> #} | ||
<div class="mt-4"> | ||
<h5 class="mb-1">Active Few-Shot Fine-Tuning</h5> | ||
<p class="mb-1" style="font-size: 0.8em;">Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause</p> | ||
<p style="font-size: 0.8em;">Preprint</p> | ||
</div> | ||
<div> | ||
<h5 class="mb-1">Information-based Transductive Active Learning</h5> | ||
<p class="mb-1" style="font-size: 0.8em;">Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause</p> | ||
<p style="font-size: 0.8em;">Preprint</p> | ||
</div> | ||
{# <div class="list-group-item my-2"> | ||
<h5 class="mb-1">Title: Advances in Quantum Computing</h5> | ||
<p class="mb-1">Authors: Albert Einstein, Niels Bohr</p> | ||
<small>Year: 2020, Venue: Proceedings of the National Academy of Sciences</small> | ||
</div> | ||
<div class="list-group-item my-2"> | ||
<h5 class="mb-1">Title: Exploring the Universe: Dark Matter and Dark Energy</h5> | ||
<p class="mb-1">Authors: Carl Sagan, Vera Rubin</p> | ||
<small>Year: 2019, Venue: Journal of Cosmology and Astroparticle Physics</small> | ||
</div> #} | ||
{# </div> #} | ||
</div> | ||
</main> | ||
<footer class="d-none d-md-block"> | ||
<div class="text-center"> | ||
Authors: <a href="https://jonhue.github.io">Jonas Hübotter</a>, <a href="https://sukhijab.github.io">Bhavya Sukhija</a>, <a href="https://lenarttreven.github.io">Lenart Treven</a>, <a href="https://yas.pub">Yarden As</a>, and <a href="https://las.inf.ethz.ch/krausea">Andreas Krause</a> | ||
</div> | ||
|
||
{% include "resources/_github-button.html" %} | ||
</footer> | ||
</body> | ||
</html> |
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