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Include docs in the main repository
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‎.github/workflows/docs.yml

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name: Deploy Docs site to Pages
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on:
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push:
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branches: [main]
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workflow_dispatch:
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permissions:
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contents: read
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pages: write
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id-token: write
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concurrency:
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group: pages
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cancel-in-progress: false
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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with:
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fetch-depth: 0 # Not needed if lastUpdated is not enabled
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- name: Setup Bun
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uses: oven-sh/setup-bun@v1 # Uncomment this if you're using Bun
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- name: Setup Pages
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uses: actions/configure-pages@v4
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- name: Navigate to docs directory
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run: cd docs
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- name: Install dependencies
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run: bun install
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- name: Build with VitePress
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run: bun run docs:build
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- name: Upload artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: .vitepress/dist
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deploy:
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environment:
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name: github-pages
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url: ${{ steps.deployment.outputs.page_url }}
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needs: build
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runs-on: ubuntu-latest
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name: Deploy
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steps:
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4

‎composer.json

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"scripts": {
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"test": "vendor/bin/pest",
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"test:coverage": "XDEBUG_MODE=coverage ./vendor/bin/pest --coverage"
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},
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"archive": {
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"exclude": ["/docs"]
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}
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}

‎docs/.gitignore

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### OSX ###
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# General
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.DS_Store
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.AppleDouble
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.LSOverride
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# Thumbnails
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._*
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# Files that might appear in the root of a volume
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.DocumentRevisions-V100
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.fseventsd
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.Spotlight-V100
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.TemporaryItems
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.Trashes
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.VolumeIcon.icns
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.com.apple.timemachine.donotpresent
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.idea
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# Directories potentially created on remote AFP share
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.AppleDB
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.AppleDesktop
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Network Trash Folder
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Temporary Items
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.apdisk
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### Node ###
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# Logs
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logs
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*.log
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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lerna-debug.log*
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# Dependency directories
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node_modules/
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jspm_packages/
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# dotenv environment variables file
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.env
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.env.test
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# vitepress build output
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.vitepress/dist
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.vitepress/cache

‎docs/.vitepress/config.mts

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import {defineConfig} from 'vitepress'
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// https://vitepress.dev/reference/site-config
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export default defineConfig({
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title: "Transformers PHP",
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description: "State-of-the-art Machine Learning for PHP. Run Transformers in PHP",
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base: "/transformers-docs/",
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themeConfig: {
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// https://vitepress.dev/reference/default-theme-config
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nav: [
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{text: 'Home', link: '/'},
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{text: 'Docs', link: '/docs/'},
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{
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text: '0.1.x',
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items: [
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{
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text: 'Changelog',
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link: 'https://github.com/CodeWithKyrian/transformers-php/blob/main/CHANGELOG.md'
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},
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{
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text: 'Contributing',
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link: 'https://github.com/CodeWithKyrian/transformers-php/blob/main/.github/contributing.md'
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},
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]
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}
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],
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sidebar: [
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{
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text: 'Getting Started',
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collapsed: false,
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items: [
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{text: 'Introduction', link: '/docs/'},
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{text: 'Getting Started', link: '/docs/getting-started'},
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{text: 'Basic Usage', link: '/docs/basic-usage'},
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{text: 'Configuration', link: '/docs/configuration'},
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]
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},
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{
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text: 'Pipelines',
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collapsed: false,
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link: '/docs/pipelines',
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items: [
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{text: 'Text Classification', link: '/docs/text-classification'},
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{text: 'Fill Mask', link: '/docs/fill-mask'},
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{text: 'Zero Shot Classification', link: '/docs/zero-shot-classification'},
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{text: 'Question Answering', link: '/docs/question-answering'},
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{text: 'Token Classification', link: '/docs/token-classification'},
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{text: 'Feature Extraction', link: '/docs/feature-extraction'},
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{text: 'Text to Text Generation', link: '/docs/text-to-text-generation'},
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{text: 'Translation', link: '/docs/translation'},
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{text: 'Summarization', link: '/docs/summarization'},
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{text: 'Text Generation', link: '/docs/text-generation'},
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]
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},
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{
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text: 'Advanced Usage',
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collapsed: false,
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items: [
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{text: 'Auto Models', link: '/docs/auto-models'},
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{text: 'Auto Tokenizers', link: '/docs/auto-tokenizers'},
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]
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},
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{
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text: 'Utilities',
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collapsed: false,
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items: [
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{text: 'Generation', link: '/docs/generation'},
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]
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}
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],
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socialLinks: [
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{icon: 'github', link: 'https://github.com/CodeWithKyrian/transformers-php'},
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{icon: 'twitter', link: 'https://twitter.com/CodeWithKyrian'}
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],
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footer: {
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message: 'Released under the MIT License.',
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copyright: 'Copyright © 2024 <a href="https://github.com/yyx990803">Kyrian Obikwelu</a>'
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},
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editLink: {
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pattern: 'https://github.com/vuejs/vitepress/edit/main/docs/:path',
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text: 'Edit this page on GitHub'
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},
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search: {
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provider: 'local'
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}
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},
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cleanUrls: true,
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lastUpdated: true,
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markdown: {
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theme: {light: 'min-light', dark: 'min-dark'},
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}
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})

‎docs/README.md

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# Transformers PHP Documentation
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Welcome to the official documentation for Transformers PHP. You can find the online version of this documentation
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at [https://codewithkyrian.github.io/transformers-docs/](https://codewithkyrian.github.io/transformers-docs/).
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## Contributing
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If you would like to contribute to the documentation, create a pull request with your changes. The documentation is
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written in Markdown so it should be easy to understand and contribute to.

‎docs/bun.lockb

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‎docs/docs/basic-usage.md

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---
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outline: deep
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---
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# Basic Usage
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The quickest and most straightforward way to get started with Transformers PHP is through the pipelines API. If you're
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familiar with the Transformers library for Python, you'll find this approach quite similar. It's a user-friendly API
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that bundles a model with all the necessary preprocessing and postprocessing steps for a specific task.
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## Creating a Pipeline
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To create a pipeline, you need to specify the task you want to use it for. For example, if you want to use a pipeline
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for sentiment analysis, you can create a pipeline like this:
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```php
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use function Codewithkyrian\Transformers\Pipelines\pipeline;
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$classifier = pipeline('sentiment-analysis');
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```
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The first time you run this, Transformers PHP will download and cache the default pre-trained model for sentiment
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analysis on-the-fly. This initial setup might take a bit, but subsequent runs will be much faster.
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> [!TIP]
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> To avoid any wait time or possible hiccups with on-the-fly model downloads, it's a good idea to pre-download
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> your models. Check out the section on [pre-downloading models](/docs/getting-started#pre-download-models) for how to
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> do this.
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## Using a different model
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Each task has a default model it uses for inference. You can however specify a different model to use:
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```php
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$classifier = pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
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```
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You can also specify if the quantized model should be used or not (the default is `true`):
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```php
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$classifier = pipeline('sentiment-analysis', quantized: false);
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```
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## Using the Pipeline
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Now that you have your pipeline, using it is as simple as calling a function. Just provide the text you want to analyze:
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```php
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$result = $classifier('I love Transformers PHP!');
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```
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And voilà, you'll get the sentiment analysis result:
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```php
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['label' => 'POSITIVE', 'score' => 0.9995358059835]
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```
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You're not limited to one text at a time; you can also pass an array of texts to get multiple analyses:
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```php
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$results = $classifier([
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'I love Transformers PHP!',
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'I hate Transformers PHP!',
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]);
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```
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The output will give you a sentiment score for each text:
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```php
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[
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['label' => 'POSITIVE', 'score' => 0.99980061678407],
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['label' => 'NEGATIVE', 'score' => 0.99842234422764],
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]
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```
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## What's Next?
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Now that you've seen how easy it is to use Transformers PHP, you might want to explore the other features it offers.
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Check out the advanced usage section to learn about more advanced features like customizing the
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pipelines, using the models directly, using tokenizers, and more.

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