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At its core, TransformersPHP is crazy simple simple.simple.simple. Its a high-level pipeline() function that handles everything: downloading the model, preprocessing your input, running inference, and post-processing the output. One or two lines of code, and you're done.

Here's a quick example for sentiment analysis. By Sentiment analysis btw, I mean is this a happy or sad string of text. That’s a simple question to ask, but much more complicated to answer. Before having an AI toolkit to answer that, it would have been a lot more than 2 simple lines. So lets see how we are doing that today.

use function Codewithkyrian\Transformers\Pipelines\pipeline;

$pipe = pipeline('sentiment-analysis');
$result = $pipe("I love TransformersPHP!");

// Returns: ['label' => 'POSITIVE', 'score' => 0.9998...]

Now pay close attention, because that is incredibly modular. You can swap in any compatible model, like a multilingual one or a quantized version for speed, and do WAAAY more than setting a boolean isHappy. TransformersPHP supports tons of tasks: text classification (I. E. From this list of categories where does this belong?, translation, summarization, question answering, zero-shot classification (aka making categories from nothing), text generation (yes, even small Llama models), image-to-text, object detection, and more.

Now, the cool part—what's under the hood? TransformersPHP doesn't reinvent the wheel. It runs models in the ONNX format (Open Neural Network Exchange), a universal standard that works no matter if the model was originally trained in PyTorch, TensorFlow, or JAX.

Inference happens through ONNX Runtime, a blazing-fast engine optimized for CPU and hardware acceleration. The PHP side connects to it using the PHP FFI extension (Foreign Function Interfa...