spacy-curated-transformers 0.2.1

Curated transformer models for spaCy pipelines

💫 🤖 spaCy Curated Transformers

This package provides spaCy components and architectures to use a curated set of transformer models via curated-transformers in spaCy.

Features

• Use pretrained models based on one of the following architectures to power your spaCy pipeline:
  • ALBERT
  • BERT
  • CamemBERT
  • RoBERTa
  • XLM-RoBERTa
• All the nice features supported by spacy-transformers such as support for Hugging Face Hub, multi-task learning, the extensible config system and out-of-the-box serialization
• Deep integration into spaCy, which lays the groundwork for deployment-focused features such as distillation and quantization
• Minimal dependencies

⏳ Installation

Installing the package from pip will automatically install all dependencies.

pip install spacy-curated-transformers

🚀 Quickstart

An example project is provided in the project directory.

📖 Documentation

• 📘 Layers and Model Architectures: Power spaCy components with custom neural networks
• 📗 CuratedTransformer: Pipeline component API reference
• 📗 Transformer architectures: Architectures and registered functions

Bug reports and other issues

Please use spaCy's issue tracker to report a bug, or open a new thread on the discussion board for any other issue.