Introducing spaCy v2.3

· by Adriane Boyd & Matthew Honnibal· ~10 min. read

spaCy now speaks Chinese, Japanese, Danish, Polish and Romanian! Version 2.3 of the spaCy Natural Language Processing library adds models for five new languages. We’ve also updated all 15 model families with word vectors and improved accuracy, while also decreasing model size and loading times for models with vectors.

This is the last major release of v2, by the way. We’ve been working hard on spaCy v3, which comes with a lot of cool improvements, especially for training, configuration and custom modeling. We’ll start making prereleases on spacy-nightly soon, so stay tuned.

New languages

spaCy v2.3 provides new model families for five languages: Chinese, Danish, Japanese, Polish and Romanian. The Chinese and Japanese language models are the first provided models that use external libraries for word segmentation rather than spaCy’s tokenizer.

Chinese

The new Chinese models use pkuseg for word segmentation and ship with a custom model trained on OntoNotes with a token accuracy of 94.6%. Users can initialize the tokenizer with both pkuseg and custom models and customize the user dictionary. Details can be found in the Chinese docs. The Chinese tokenizer continues to support jieba as the default word segmenter along with character-based segmentation as in v2.2.

Japanese

The updated Japanese language class switches to SudachiPy for word segmentation and part-of-speech tagging. Using sudachipy greatly simplifies installing spaCy for Japanese, which is now possible with a single command: pip install spacy[ja]. More details are in the Japanese docs.

Model Performance

Following our usual convention, the sm, md and lg models differ in their word vectors. The lg models include one word vector for most words in the training data, while the md model prunes the vectors table to only include entries for the 20,000 most common words, mapping less frequent words to the most similar vector in the reduced table. The sm models do not use pretrained vectors.

LanguageModelSizeTAGUASLASENTS F
Chinesezh_core_web_sm45 MB89.6368.5563.2166.57
zh_core_web_md75 MB90.2369.3964.4368.52
zh_core_web_lg575 MB90.5569.7764.9969.33
Danishda_core_news_sm16 MB92.7980.4875.6572.79
da_core_news_md46 MB94.1382.7178.9881.45
da_core_news_lg546 MB94.9582.5378.9982.73
Japaneseja_core_news_sm7 MB97.3088.6886.8759.93
ja_core_news_md37 MB97.3089.2687.7667.68
ja_core_news_lg526 MB97.3088.9487.5570.48
Polishpl_core_news_sm46 MB98.0385.6178.0981.32
pl_core_news_md76 MB98.2890.4184.4784.68
pl_core_news_lg576 MB98.4590.8085.5285.67
Romanianro_core_news_sm13 MB95.6587.2079.7971.05
ro_core_news_md43 MB96.3288.6981.7775.42
ro_core_news_lg545 MB96.7888.8782.0576.71

The training data for Danish, Japanese and Romanian is relatively small, so the pretrained word vectors improve accuracy quite a lot, in particular for NER. The Chinese model uses a larger training corpus, but word segmentation errors may make the word vectors less effective. Word segmentation accuracy also explains some of the lower scores for Chinese, as the model has to get the word segmentation correct before it can be scored as accurate on any of the subsequent tasks.

Word vectors for all model families

All model families now include medium and large models with 20k and 500k unique vectors respectively. For most languages, spaCy v2.3 introduces custom word vectors trained using spaCy’s language-specific tokenizers on data from OSCAR and Wikipedia. The vectors are trained using FastText with the same settings as FastText’s word vectors (CBOW, 300 dimensions, character n-grams of length 5).

In particular for languages with smaller training corpora, the addition of word vectors greatly improves the model accuracy. For example, the Lithuanian tagger increases from 81.7% for the small model (no vectors) to 89.3% for the large model. The parser increases by a similar margin and the NER F-score increases from 66.0% to 70.1%. For German, updating the word vectors increases the scores for the medium model for all components by 1.5 percentage points across the board.

Model compatibility

Remember that models trained with v2.2 will be incompatible with the new version. To find out if you need to update your models, you can run python -m spacy validate. If you’re using your own custom models, you’ll need to retrain them with the new version.

Updated training data

All spaCy training corpora based on Universal Dependencies corpora have been updated to UD v2.5 (v2.6 for Japanese, v2.3 for Polish). The updated data improves the quality and size of the training corpora, increasing the tagger and parser accuracy for all provided models. For example, the Dutch training data is extended to include both UD Dutch Alpino and LassySmall, which improves the tagger and parser scores for the small models by 3%, and the addition of the new word vectors improve the scores further by 3-5%.

Fine-grained POS tags

As a result of the updates, many of the fine-grained part-of-speech tag sets will differ from v2.2 models. The coarse-grained tag-set remains the same, although there are some minor differences in how they are calculated from the fine-grained tags.

For French, Italian, Portuguese and Spanish, the fine-grained part-of-speech tag sets contain new merged tags related to contracted forms, such as ADP_DET for French "au", which maps to UPOS ADP based on the head "à". This increases the accuracy of the models by improving the alignment between spaCy’s tokenization and Universal Dependencies multi-word tokens used for contractions.

Smaller models and faster loading times

The medium model packages with 20k vectors are at least 2× smaller than in v2.2, the large English model is 120M smaller, and the loading times are 2-4× faster for all models with vectors. To achieve this, models no longer store derivable lexeme attributes such as lower and is_alpha and the remaining lexeme attributes (norm, cluster and prob) have been moved to spacy-lookups-data.

If you’re training new models, you’ll probably want to install spacy-lookups-data for normalization and lemmatization tables! The provided models include the norm lookup tables for use with the core pipeline components, but the optional cluster and prob features are now only available through spacy-lookups-data.

Free online course and tutorials

We’re also proud to announce updates and translations of our online course, “Advanced NLP with spaCy”. We’ve made a few small updates to the English version, including new videos to go with the interactive exercises. It’s really the translations we’re excited about though. We have translations into Japanese, German and Spanish, with Chinese, French and Russian soon to come.

spacy v2 3 spacy course

Speaking of videos, you should also check out Sofie’s tutorial on training a custom entity linking model with spaCy. You can find the code and data in our growing projects repository.

Another cool video to check out is the new episode of Vincent Warmerdam’s “Intro to NLP with spaCy” . The series lets you sit beside Vincent as he works through an example data science project using spaCy. In episode 5, “Rules vs. Machine Learning”, Vincent uses spaCy’s rule-based matcher to probe the decisions of the NER model he trained previously, using the rules to understand the model’s behavior and figure out how to improve the training data to get better results.

What’s next?

spaCy v2.3 is the last big release of v2. We’ve been working hard on v3, which we expect to start publishing prereleases of in the next few weeks. spaCy v3 comes with a lot of cool improvements, especially for training, configuration and custom modeling. The training and data formats are the main thing we’ve taken the opportunity to fix, so v3 will have some breaking changes, but don’t worry — it’s nothing like the big transformations seen in libraries like TensorFlow or Angular. It should be pretty easy to upgrade, but we’ve still tried to backport as much as possible into v2.3, so you can use it right away. We’ll also continue to make maintenance releases of v2.3 with bug fixes as they come in.

We also have a big release of our annotation tool Prodigy pretty much ready to go. In addition to the spaCy v2.3 update (giving you all the new models), Prodigy v1.10 comes with a new annotation interface for tasks like relation extraction and coreference resolution, full-featured audio and video annotation (including recipes using pyannote.audio models in the loop), a new and improved manual image UI, more options for NER annotation, new recipe callbacks, and lots more. To get notified when it’s ready, follow us on Twitter!

Resources

  • About the author

    Adriane Boyd

    Adriane is a computational linguist who has been engaged in research since 2005, completing her PhD in 2012. She has extensive experience in quality control for linguistic annotation, parsing, and NLP for non-standard language.

  • About the author

    Matthew Honnibal

    Matthew is a leading expert in AI technology. He completed his PhD in 2009, and spent a further 5 years publishing research on state-of-the-art NLP systems. He left academia in 2014 to write spaCy and found Explosion.