Introduction to spacy-transformers
The spacy-transformers project is a valuable resource for those working with natural language processing (NLP) in Python. By integrating the power of transformer models such as BERT, XLNet, and GPT-2 into the spaCy library, this package offers a means to leverage state-of-the-art NLP techniques for a variety of applications.
Overview
Developed by Explosion AI, spacy-transformers connects Hugging Face's transformers library with spaCy, enhancing spaCy's capabilities with advanced transformer models. It allows users to integrate pre-trained models easily into spaCy pipelines, providing the benefit of multi-task learning and efficient processing.
Key Features
- Pretrained Transformer Models: Users can embed transformer models like BERT, RoBERTa, and XLNet into their spaCy structures.
- Multi-Task Learning: The package supports multi-task learning by backpropagating from multiple components to a single transformer model.
- Training and Config System: It utilizes spaCy v3's robust and flexible configuration for model training.
- Tokenization Alignment: Outputs from transformers are automatically aligned with spaCy's tokenization, ensuring seamless integration.
- Customization: Users can customize what transformer data to retain in a document and adjust processing for lengthy documents.
- Serialization: Provides built-in serialization and packaging for models, aiding in sharing and deployment.
Installation
To get started with spacy-transformers, one can easily install it via pip. It requires Python 3.6+, PyTorch v1.5+, and spaCy v3.0+. The following command installs the package and its dependencies:
pip install 'spacy[transformers]'
For GPU support, installation involves specifying the CUDA version:
spacy[transformers,cuda92] # Example for CUDA9.2
spacy[transformers,cuda100] # Example for CUDA10.0
For any complications with PyTorch installation, it’s recommended to follow the official instructions tailored to specific systems and requirements.
Documentation
The package has been significantly reworked to align with spaCy v3.0. It’s crucial to refer to the revised documentation due to these changes:
- Embeddings and Transformers
- Training Pipelines
- Model Architectures
- Transformer API
- Architecture Details
Pretrained Text and Token Classification
Though the transformer component in spacy-transformers doesn’t support task-specific heads by default, users can opt for the spacy-huggingface-pipelines to use predictions from certain pretrained models for specific tasks.
Reporting Issues
For reporting bugs or other concerns, the spaCy community can utilize the issue tracker or engage in discussions on its discussion board.
The spacy-transformers package is thus a comprehensive toolkit for those aiming to incorporate advanced NLP models into their spaCy applications seamlessly.