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textualization/ropherta
=======================

Compute RoBERTa embeddings using ONNX framework.

v0.0.11(1y ago)41123[1 PRs](https://github.com/Textualization/Ropherta/pulls)1MITPHP

Since Aug 8Pushed 1y ago1 watchersCompare

[ Source](https://github.com/Textualization/Ropherta)[ Packagist](https://packagist.org/packages/textualization/ropherta)[ Fund](https://ko-fi.com/textualization)[ RSS](/packages/textualization-ropherta/feed)WikiDiscussions main Synced today

READMEChangelogDependencies (3)Versions (12)Used By (1)

Compute RoBERTa embeddings in PHP using ONNX framework.
=======================================================

[](#compute-roberta-embeddings-in-php-using-onnx-framework)

This brings the power of Transformers to the PHP world.

Installation
------------

[](#installation)

Add this project to your dependencies

```
composer require textualization/ropherta
composer update

```

Before using it, you will need to install the ONNX framework:

```
composer exec -- php -r "require 'vendor/autoload.php'; OnnxRuntime\Vendor::check();"

```

and download the RoBERTa ONNX model (this takes a while, the model is 477Mb in size):

```
composer exec -- php -r "require 'vendor/autoload.php'; Textualization\Ropherta\Vendor::check();"

```

Computing embeddings
--------------------

[](#computing-embeddings)

```
$model = new RophertaModel();

$emb = $model->embeddings("Text");
```

Check `\Textualization\Ropherta\Distances` to check whether two embeddings are closer to each other.

Using custom embeddings
-----------------------

[](#using-custom-embeddings)

```
$model = new RophertaModel("/path/to/model.onnx");
$emb = $model->embeddings("Text");
```

To fine-tune a model you will need a large amount of in-domain text and use Python in a machine with a GPU. See [tuning](tuning/) for details.

Sponsors
--------

[](#sponsors)

We thank our sponsor:

[![](https://camo.githubusercontent.com/fa0fa4bf60ec8eeb4d2a15311000fc2b3767fb96f788f6bf502d1117a734a221/68747470733a2f2f65766f6c75646174612e636f6d2f646973706c6179323038)](https://evoludata.com/)

###  Health Score

27

—

LowBetter than 52% of packages

Maintenance32

Infrequent updates — may be unmaintained

Popularity18

Limited adoption so far

Community14

Small or concentrated contributor base

Maturity39

Early-stage or recently created project

 Bus Factor1

Top contributor holds 94.4% of commits — single point of failure

How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window.

**Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores.

**Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement.

**Maturity (30%)** — Project age, version count, PHP version support, and release stability.

###  Release Activity

Cadence

Every ~26 days

Recently: every ~19 days

Total

11

Last Release

691d ago

### Community

Maintainers

![](https://www.gravatar.com/avatar/970f356fe04869413a212efcf75d122352265e34d9cd35d85b0fe7d51ef25162?d=identicon)[textualization](/maintainers/textualization)

---

Top Contributors

[![DrDub](https://avatars.githubusercontent.com/u/315403?v=4)](https://github.com/DrDub "DrDub (17 commits)")[![benoitg](https://avatars.githubusercontent.com/u/709901?v=4)](https://github.com/benoitg "benoitg (1 commits)")

---

Tags

embeddingsmachine-learningnatural-language-processingphp-libraryroberta-model

###  Code Quality

TestsPHPUnit

### Embed Badge

![Health badge](/badges/textualization-ropherta/health.svg)

```
[![Health](https://phpackages.com/badges/textualization-ropherta/health.svg)](https://phpackages.com/packages/textualization-ropherta)
```

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