🤘 Lemmy

Lemmy is a lemmatizer for Danish 🇩🇰 . It comes already trained on Dansk Sprognævns (DSN) word list (‘fuldformliste’) and the Danish Universal Dependencies and is ready for use. Lemmy also supports training on your own dataset.

The model currently included in Lemmy was evaluated on the Danish Universal Dependencies dev dataset and scored an accuracy > 99%.

You can use Lemmy as a spaCy extension, more specifcally a spaCy pipeline component. This is highly recommended and makes the lemmas easily accessible from the spaCy tokens. Lemmy makes use of POS tags to predict the lemmas. When wired up to the spaCy pipeline, Lemmy has the benefit of using spaCy’s builtin POS tagger.

Lemmy can also by used without spaCy, as a standalone lemmatizer. In that case, you will have to provide the POS tags. Alternatively, you can train a Lemmy model which does not depend on POS tags, though most likely the accuracy will suffer.

Lemmy is heavily inspired by the CST Lemmatizer for Danish.

Install

pip install lemmy

Usage

import da_custom_model as da # name of your spaCy model
import lemmy.pipe
nlp = da.load()

# create an instance of Lemmy's pipeline component for spaCy
pipe = lemmy.pipe.load('da')

# add the comonent to the spaCy pipeline.
nlp.add_pipe(pipe, after='tagger')

# lemmas can now be accessed using the `._.lemma` attribute on the tokens
nlp("akvariernes")[0]._.lemma

Training

The notebooks folder contains examples showing how to train your own model using Lemmy.