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Also, my team chat is currently filled with people being extremely stoked about the SpaCy + FastAPI support! Really hope FastAPI replaces Flask sooner rather than later.
> spaCy is a library for advanced Natural Language Processing in Python and Cython.
Spacy3 practically covers 90% of NLP use-cases with near SOTA performance. The only reason to not use it would be if you are literally pushing the boundaries of NLP or building something super specialized.
Hugging Face and Spacy (also Pytorch, but duh) are saving millions of dollars in man hours for companies around the world. They've been a revelation.
Ok so I’ve evaluated spacy a few years ago, but nowadays we’re using huggingface’s transformers / tokenizers / etc to train our own language models + fine tuned models. I see there’s now transformer based pipeline support, how do the two relate? Phrased differently, how does spacy fit in with today’s world of transformers? Would it still be interesting for me?
I have lots of experience with both, and I use both together for different use cases. SpaCy fills the need of predictable/explainable pattern matching and NER - and is very fast and reasonably accurate on a CPU. Huggingface fills the need for task based prediction when you have a GPU.
With model distillation, you can make models that annotate hundreds of sentences per second on a single CPU with a library like Huggingface Transformers.
For instance, one of my distilled Dutch multi-task syntax models (UD POS, language-specific POS, lemmatization, morphology, dependency parsing) annotates 316 sentences per second with 4 threads on a Ryzen 3700X. This distilled model has virtually no loss in accuracy compared to the finetuned XLM-RoBERTa base model.
I don't use Huggingface Transformers, but ported some of their implementations to Rust [1], but that should not make a big difference since all the heavy lifting happens in C++ in libtorch anyway.
tl;dr: it is not true that tranformers are only useful for GPU prediction. You can get high CPU prediction speeds with some tricks (distillation, length-based bucketing in batches, using MKL, etc.).
[1] https://github.com/tensordot/syntaxdot/tree/main/syntaxdot-t...
Please note that Explosion does not like redistribution of SpaCy, they expect everyone to only use the builds they produce, so it would not be a good idea to package it for your favourite distro.
Please note that Explosion does not like redistribution of SpaCy, they expect everyone to only use the builds they produce, so it would not be a good idea to package it for your favourite distro.
Pretty poor choice of license if they wanted me to care about their builds, tbh.
Please note that Explosion does not like redistribution of SpaCy, they expect everyone to only use the builds they produce, so it would not be a good idea to package it for your favourite distro.
We of course encourage anyone to clone the repo or install from an sdist if they want to compile from source. In fact you can do the following:
git clone https://github.com/explosion/spaCy
cd spaCy
make
I have been using Spacy3 nightly for a while now. This is game changing. Spacy3 practically covers 90% of NLP use-cases with near SOTA performance. The only reason to not use it would be if you are literally pushing the boundaries of NLP or building something super specialized. Hugging Face and Spacy (also Pytorch, but duh) are saving millions of dollars in man hours for companies around the world. They've been a rev…
As someone that's worked on some rather intensive NLP implementations, Spacy 3.0 and HuggingFace both represent the culmination of a technological leap in NLP that started a few years ago with the advent of transfer learning in NLP. The level of accessibility to the masses these libraries offer is game-changing and democratizing.