feat: Rename package name to TransformersPHP to avoid naming conflict (Hugging Face)

- Updates package name from Transformers PHP (transformers-php) to TransformersPHP.
- This change prevents potential confusion and conflicts with the popular Hugging Face Transformers library.

Author: CodeWithKyrian

README.md

@@ -1,27 +1,27 @@
 <h1 align="center">
-   Transformers PHP
+   TransformersPHP
 </h1>
 
 <h3 align="center">
     <p>State-of-the-art Machine Learning for PHP</p>
 </h3>
 
-Transformers PHP is designed to be functionally equivalent to the Python library, while still maintaining the same level
+TransformersPHP is designed to be functionally equivalent to the Python library, while still maintaining the same level
 of performance and ease of use. This library is built on top of the Hugging Face's Transformers library, which provides
 thousands of pre-trained models in 100+ languages. It is designed to be a simple and easy-to-use library for PHP
 developers using a similar API to the Python library. These models can be used for a variety of tasks, including text
 generation, summarization, translation, and more.
 
-Transformers PHP uses [ONNX Runtime](https://onnxruntime.ai/) to run the models, which is a high-performance scoring
+TransformersPHP uses [ONNX Runtime](https://onnxruntime.ai/) to run the models, which is a high-performance scoring
 engine for Open Neural Network Exchange (ONNX) models. You can easily convert any PyTorch or TensorFlow model to ONNX
-and use it with Transformers PHP using [🤗 Optimum](https://github.com/huggingface/optimum#onnx--onnx-runtime).
+and use it with TransformersPHP using [🤗 Optimum](https://github.com/huggingface/optimum#onnx--onnx-runtime).
 
 TO learn more about the library and how it works, head over to
 our [extensive documentation](https://codewithkyrian.github.io/transformers-php/introduction).
 
 ## Quick tour
 
-Because Transformers PHP is designed to be functionally equivalent to the Python library, it's super easy to learn from
+Because TransformersPHP is designed to be functionally equivalent to the Python library, it's super easy to learn from
 existing Python or Javascript code. We provide the `pipeline` API, which is a high-level, easy-to-use API that groups
 together a model with its necessary preprocessing and postprocessing steps.
 
@@ -109,7 +109,7 @@ Next, you must run the installation/initialize command to download the shared li
 
 ## PHP FFI Extension
 
-Transformers PHP uses the PHP FFI extension to interact with the ONNX runtime. The FFI extension is included by default
+TransformersPHP uses the PHP FFI extension to interact with the ONNX runtime. The FFI extension is included by default
 in PHP 7.4 and later, but it may not be enabled by default. If the FFI extension is not enabled, you can enable it by
 uncommenting(remove the `;` from the beginning of the line) the
 following line in your `php.ini` file:
@@ -133,13 +133,13 @@ documentation : [https://codewithkyrian.github.io/transformers-php](https://code
 
 ## Usage
 
-By default, Transformers PHP uses hosted pretrained ONNX models. For supported tasks, models that have been converted to
+By default, TransformersPHP uses hosted pretrained ONNX models. For supported tasks, models that have been converted to
 work with [Xenova's Transformers.js](https://huggingface.co/models?library=transformers.js) on HuggingFace should work
-out of the box with Transformers PHP.
+out of the box with TransformersPHP.
 
 ## Configuration
 
-You can configure the behaviour of the Transformers PHP library as follows:
+You can configure the behaviour of the TransformersPHP library as follows:
 
 ```php
 use Codewithkyrian\Transformers\Transformers;
@@ -159,16 +159,16 @@ the [documentation](https://codewithkyrian.github.io/transformers-php/configurat
 
 ## Convert your models to ONNX
 
-Transformers PHP only works with ONNX models, therefore, you must convert your PyTorch, TensorFlow or JAX models to
+TransformersPHP only works with ONNX models, therefore, you must convert your PyTorch, TensorFlow or JAX models to
 ONNX. It is recommended to use [🤗 Optimum](https://huggingface.co/docs/optimum) to perform the conversion and
 quantization of your model.
 
 ## Pre-Download Models
 
-By default, Transformers PHP automatically retrieves model weights (ONNX format) from the Hugging Face model hub when
+By default, TransformersPHP automatically retrieves model weights (ONNX format) from the Hugging Face model hub when
 you first use a pipeline or pretrained model. This can lead to a slight delay during the initial use. To improve the
 user experience, it's recommended to pre-download the models you intend to use before running them in your PHP
-application, especially for larger models. One way to do that is run the request once manually, but Transformers PHP
+application, especially for larger models. One way to do that is run the request once manually, but TransformersPHP
 also comes with a command line tool to help you do just that:
 
 ```bash
@@ -195,7 +195,7 @@ Explanation of Arguments:
 
 ## Supported tasks/models
 
-This package is a WIP, but here's a list of tasks and architectures currently tested and supported by Transformers PHP.
+This package is a WIP, but here's a list of tasks and architectures currently tested and supported by TransformersPHP.
 
 ### Tasks
 

docs/.vitepress/config.mts

@@ -2,7 +2,7 @@ import {defineConfig} from 'vitepress'
 
 // https://vitepress.dev/reference/site-config
 export default defineConfig({
-    title: "Transformers PHP",
+    title: "TransformersPHP",
     description: "State-of-the-art Machine Learning for PHP. Run Transformers in PHP",
     base: "/transformers-php/",
     themeConfig: {

docs/README.md

@@ -1,6 +1,6 @@
-# Transformers PHP Documentation
+# TransformersPHP Documentation
 
-Welcome to the official documentation for Transformers PHP. You can find the online version of this documentation
+Welcome to the official documentation for TransformersPHP. You can find the online version of this documentation
 at [https://codewithkyrian.github.io/transformers-docs/](https://codewithkyrian.github.io/transformers-docs/).
 
 ## Contributing

docs/basic-usage.md

@@ -4,7 +4,7 @@ outline: deep
 
 # Basic Usage
 
-The quickest and most straightforward way to get started with Transformers PHP is through the pipelines API. If you're
+The quickest and most straightforward way to get started with TransformersPHP is through the pipelines API. If you're
 familiar with the Transformers library for Python, you'll find this approach quite similar. It's a user-friendly API
 that bundles a model with all the necessary preprocessing and postprocessing steps for a specific task.
 
@@ -19,7 +19,7 @@ use function Codewithkyrian\Transformers\Pipelines\pipeline;
 $classifier = pipeline('sentiment-analysis');
 ```
 
-The first time you run this, Transformers PHP will download and cache the default pre-trained model for sentiment
+The first time you run this, TransformersPHP will download and cache the default pre-trained model for sentiment
 analysis on-the-fly. This initial setup might take a bit, but subsequent runs will be much faster.
 
 > [!TIP]
@@ -46,7 +46,7 @@ $classifier = pipeline('sentiment-analysis', quantized: false);
 Now that you have your pipeline, using it is as simple as calling a function. Just provide the text you want to analyze:
 
 ```php
-$result = $classifier('I love Transformers PHP!');
+$result = $classifier('I love TransformersPHP!');
 ```
 
 And voilà, you'll get the sentiment analysis result:
@@ -59,8 +59,8 @@ You're not limited to one text at a time; you can also pass an array of texts to
 
 ```php
 $results = $classifier([
-    'I love Transformers PHP!',
-    'I hate Transformers PHP!',
+    'I love TransformersPHP!',
+    'I hate TransformersPHP!',
 ]);
 ```
 
@@ -75,6 +75,6 @@ The output will give you a sentiment score for each text:
 
 ## What's Next?
 
-Now that you've seen how easy it is to use Transformers PHP, you might want to explore the other features it offers.
+Now that you've seen how easy it is to use TransformersPHP, you might want to explore the other features it offers.
 Check out the advanced usage section to learn about more advanced features like customizing the
 pipelines, using the models directly, using tokenizers, and more.

docs/configuration.md

@@ -4,12 +4,12 @@ outline: deep
 
 # Configuration
 
-You can configure Transformers PHP for your specific use case. This page provides an overview of the available
+You can configure TransformersPHP for your specific use case. This page provides an overview of the available
 configuration options.
 
 ## Overview
 
-Configuring Transformers PHP involves setting parameters such as the cache directory, the remote host for downloading
+Configuring TransformersPHP involves setting parameters such as the cache directory, the remote host for downloading
 models, and the remote path template. These settings allow you to tailor how and where models are stored and retrieved.
 
 ```php
@@ -31,13 +31,13 @@ use Codewithkyrian\Transformers\Transformers;
 
 ### `setCacheDir(?string $cacheDir)`
 
-The cache directory is where Transformers PHP stores the downloaded ONNX models. By default, this is set to
+The cache directory is where TransformersPHP stores the downloaded ONNX models. By default, this is set to
 the `.transformers-cache/models` directory from the root of your project. Please ensure this directory is writable by
 your application.
 
 ### `setRemoteHost(string $remoteHost)`
 
-The remote host defines where Transformers PHP looks to download model files. The default host
+The remote host defines where TransformersPHP looks to download model files. The default host
 is https://huggingface.co, which is where Hugging Face hosts its models. If you host your models on a different server
 or use a private repository for models, you can set this to the base URL of that server.
 
@@ -104,7 +104,7 @@ establish the default configuration, else, you will not be able to run any infer
 ### Standalone PHP Projects
 
 In a standalone PHP project, the best place to add global configuration is in your project's bootstrap or initialization
-script. This script should run before any feature utilizing the Transformers PHP library is called.
+script. This script should run before any feature utilizing the TransformersPHP library is called.
 
 ::: code-group
 
@@ -175,6 +175,6 @@ public function boot()
 
 ## Next Steps
 
-Now that you've learned how to configure Transformers PHP, you can start using the library to download and use
+Now that you've learned how to configure TransformersPHP, you can start using the library to download and use
 pre-trained ONNX models. For more information on how to use the library, check out
 the [Getting Started](getting-started.md) guide.

docs/getting-started.md

@@ -6,7 +6,7 @@ outline: deep
 
 ## Prerequisites
 
-Before installing Transformers PHP, ensure your system meets the following requirements:
+Before installing TransformersPHP, ensure your system meets the following requirements:
 
 - PHP 8.1 or above
 - Composer
@@ -33,13 +33,13 @@ models:
 > platform where the code will be executed. For example, if you're using a Docker container, run the `install` command
 > inside that container.
 
-This command sets up everything you need to start using pre-trained ONNX models with Transformers PHP.
+This command sets up everything you need to start using pre-trained ONNX models with TransformersPHP.
 
 ## Pre-Download Models
 
-By default, Transformers PHP automatically retrieves model weights (ONNX format) from the Hugging Face model hub when
+By default, TransformersPHP automatically retrieves model weights (ONNX format) from the Hugging Face model hub when
 you first use a pipeline or pretrained model. To save time and enhance the user experience, it's a good idea to download
-the ONNX model weights ahead of time, especially for larger models. Transformers PHP includes a command-line tool to
+the ONNX model weights ahead of time, especially for larger models. TransformersPHP includes a command-line tool to
 facilitate this:
 
 ```bash
@@ -70,21 +70,21 @@ Arguments:
       can use the shorthand `-q` instead of `--quantized`. Example: `--quantized=false`, `-q false`.
 
 The `download` command will download the model weights and save them to the cache directory. The next time you use the
-model, Transformers PHP will use the cached weights instead of downloading them again.
+model, TransformersPHP will use the cached weights instead of downloading them again.
 
 > [!CAUTION]
 > Remember to add your cache directory to your `.gitignore` file to avoid committing the downloaded models to your git
 > repository.
 
 ## Use Custom Models
 
-Since Transformers PHP operates exclusively with ONNX models, you'll need to convert any machine learning models you've
+Since TransformersPHP operates exclusively with ONNX models, you'll need to convert any machine learning models you've
 developed or plan to use from PyTorch, TensorFlow, or JAX into the ONNX format.
 
 For this conversion process, we recommend using
 the [conversion script](https://github.com/xenova/transformers.js/blob/main/scripts/convert.py)
 provided by the Transformers.js project. This script is designed to convert models from PyTorch, TensorFlow, and JAX to
-ONNX format, and most importantly, outputs it in a folder structure that is compatible with Transformers PHP. Behind the
+ONNX format, and most importantly, outputs it in a folder structure that is compatible with TransformersPHP. Behind the
 scenes, the script uses [🤗 Optimum](https://huggingface.co/docs/optimum) from Hugging Face to convert and quantize the
 models.
 
@@ -108,7 +108,7 @@ The steps for conversion are simple:
   Hugging Face account for sharing and storage.
 
 Whether you convert using the script, or the noteboook, or using TensorFlow's `tf.saved_model` or
-PyTorch's `torch.onnx.export`, just make sure the folder structure of the output is compatible with Transformers PHP.
+PyTorch's `torch.onnx.export`, just make sure the folder structure of the output is compatible with TransformersPHP.
 The script and the DOcker image already handle this for you.
 
 The folder structure should look like this:
@@ -137,7 +137,7 @@ the [Optimum documentation.](https://huggingface.co/docs/optimum/main/en/exporte
 
 ## PHP FFI Extension
 
-Transformers PHP uses the PHP FFI extension to interact with the ONNX runtime. The FFI extension is included by default
+TransformersPHP uses the PHP FFI extension to interact with the ONNX runtime. The FFI extension is included by default
 in PHP 7.4 and later, but it may not be enabled by default. To check if the FFI extension is enabled, run the following
 command:
 
@@ -165,7 +165,7 @@ After making these changes, restart your web server or PHP-FPM service, and you
 
 Just-In-Time (JIT) compilation is a feature that allows PHP to compile and execute code at runtime. JIT compilation can
 improve the performance of your application by compiling frequently executed code paths into machine code. While you
-can use Transformers PHP without JIT compilation, enabling it can provide a significant performance boost (> 2x in some
+can use TransformersPHP without JIT compilation, enabling it can provide a significant performance boost (> 2x in some
 cases).
 
 JIT compilation is available in PHP 8.0 and later, but it may not be enabled by default. To enable JIT compilation,

docs/index.md

@@ -3,7 +3,7 @@
 layout: home
 
 hero:
-  name: "Transformers PHP"
+  name: "TransformersPHP"
   text: ""
   tagline: State-of-the-art Machine Learning for PHP. Run Transformers natively in your PHP projects
   actions:

docs/introduction.md

@@ -4,18 +4,18 @@ outline: deep
 
 # Introduction
 
-## What is Transformers PHP
+## What is TransformersPHP
 
-Transformers PHP is a toolkit for PHP developers to add machine learning magic to their projects easily. You've probably
+TransformersPHP is a toolkit for PHP developers to add machine learning magic to their projects easily. You've probably
 heard about the Python library from Hugging Face, famous for doing awesome stuff with text, like summarizing long
 articles, translating between languages, and even image and audio related tasks. Transformers
 PHP brings this capability to the PHP world.
 
 ### Using Pre-trained Models
 
-The core idea behind Transformers PHP is to let you use models that are already trained. "Pre-trained models" are just
+The core idea behind TransformersPHP is to let you use models that are already trained. "Pre-trained models" are just
 machine learning models that have been fed and learned from massive amounts of text data. They're ready to go out of the
-box and can perform a wide range of tasks. With Transformers PHP, these models run directly in your PHP application.
+box and can perform a wide range of tasks. With TransformersPHP, these models run directly in your PHP application.
 That means you don't need to use external services or APIs to process your data. Everything happens locally, on your
 server.
 
@@ -30,10 +30,10 @@ different platforms, including your PHP applications.
 
 ### Inspired by the Best
 
-The development of Transformers PHP was inspired by the [Xenova/transformers](https://github.com/xenova/transformers.js)
+The development of TransformersPHP was inspired by the [Xenova/transformers](https://github.com/xenova/transformers.js)
 project, a similar initiative for JavaScript using ONNX runtime too. This shared inspiration means that most models
 prepared for use with [Xenova/transformers](https://github.com/xenova/transformers.js), are also compatible with
-Transformers PHP. It creates a seamless bridge between the machine learning world and PHP development, allowing you to
+TransformersPHP. It creates a seamless bridge between the machine learning world and PHP development, allowing you to
 leverage powerful models within your applications.
 
 ## Quick tour
@@ -95,8 +95,8 @@ let out = await pipe('I love transformers!');
 
 You can see how similar it is across languages, making it easier if you're switching between them or learning a new one.
 
-## What Transformers PHP is Not
+## What TransformersPHP is Not
 
 While the original HuggingFace Transformers library in Python is a versatile tool supporting both the training of
-machine learning models and inference (using models to make predictions), Transformers PHP allows only inference. This
-means that you cannot train new models from scratch, or fine-tune pretrained models using Transformers PHP.
+machine learning models and inference (using models to make predictions), TransformersPHP allows only inference. This
+means that you cannot train new models from scratch, or fine-tune pretrained models using TransformersPHP.