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Filtered by project: spaCy
Implementing a custom trainable component for relation extraction

Implementing a custom trainable component for relation extraction

Relation extraction refers to the process of predicting and labeling semantic relationships between named entities. In this blog post, we'll go over the process of building a custom relation extraction component using spaCy and Thinc. We'll also add a Hugging Face transformer to improve performance at the end of the post. You'll see how you can utilize Thinc's flexible and customizable system to build an NLP pipeline for biomedical relation extraction.

Blog
Victoria SlocumSofie Van Landeghem
The Tale of Bloom Embeddings and Unseen Entities

The Tale of Bloom Embeddings and Unseen Entities

The default Bloom embedding layer in spaCy is unconventional, but very powerful and efficient. We wrote about it before and showed the advantages it provides in terms of memory efficiency for our floret embeddings. Now we have released the first technical report by Explosion, where we explain Bloom embeddings in more detail and rigorously compare them to traditional embeddings. In this post we'll highlight some of our results with a special focus on unseen entities.

Blog
Sofie Van LandeghemVictoria SlocumLj MirandaÁkos Kádár
Multi hash embeddings in spaCy

Multi hash embeddings in spaCyMiranda, Kádár, Boyd, Van Landeghem, Søgaard, Honnibal (2022)

In this technical report we lay out a bit of history and introduce the embedding methods in spaCy in detail. Second, we critically evaluate the hash embedding architecture with multi-embeddings on Named Entity Recognition datasets from a variety of domains and languages. The experiments validate most key design choices behind spaCy’s embedders, but we also uncover a few surprising results.

Paper
Matthew HonnibalSofie Van LandeghemAdriane BoydÁkos KádárLj Miranda
Using spaCy with Hugging Face Transformers

Using spaCy with Hugging Face TransformersPyCon India

Transformer models like BERT have set a new standard for accuracy on almost every NLP leaderboard. However, these models are very new, and most of the software ecosystem surrounding them is oriented towards the many opportunities for further research. In this talk, Matt describes how you can now use these models in spaCy to work on real problems and the many opportunities transfer learningfor production NLP, regardless of which software packages you choose.

Talk
Matthew Honnibal
Multi-threading spaCy's parser and named entity recognizer

Multi-threading spaCy's parser and named entity recognizer

In v0.100.3, we quietly rolled out support for GIL-free multi-threading for spaCy's syntactic dependency parsing and named entity recognition models. Because these models take up a lot of memory, we've wanted to release the global interpretter lock (GIL) around them for a long time. When we finally did, it seemed a little too good to be true, so we delayed celebration — and then quickly moved on to other things. It's now past time for a write-up.

Blog
Matthew Honnibal
The triangulation of ethical leader signals using qualitative, experimental, and data science methods

The triangulation of ethical leader signals using qualitative, experimental, and data science methodsBanks, Ross, Toth, Tonidandel, Goloujeh, Dou, Wesslen (2022)

This additional text was labeled by the same coding team using Prodigy, [...] a flexible user interface tool built on top of spaCy, a leading open source library in python for natural language processing. We created a spaCy end‐to‐end project workflow including package versioning, data pre‐processing, data ingestion into a database, annotation sessions using Prodigy’s user interface, model training, model evaluation, python packaging, and visual app for testing the model.

Paper
Ryan Wesslen

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