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spaCy supports a number of transfer and multi-task learning workflows that can often help improve your pipeline’s efficiency or accuracy. Transfer learning refers to techniques such as word vector tables and language model pretraining. These techniques can be used to import knowledge from raw text into your pipeline, so that your models are able to generalize better from your annotated examples. You can convert word vectors from popular tools like FastText and Gensim, or you can load in any pre trained transformer model if you install spacy-transformers. You can also do your own language model pretraining via the spacy pre train command. You can even share your transformer or another contextual embedding model across multiple components, which can make long pipelines several times more efficient. To use transfer learning, you’ll need at least a few annotated examples for what you’re trying to predict.
Features
- Shared embedding layers
- You can share a single transformer or other tok2vec model between multiple components by adding a Transformer
- Use transformer models
- Transformer models can be used as drop-in replacements
- You can also customize how the Transformer component sets annotations
- The recommended workflow for training is to use spaCy’s config system
Project Samples
