--- language: - de tags: - flair - sequence-tagger-model - part-of-speech - tweets --- # Fine-grained POS Tagging of German Tweets This Flair model was trained on the German Tweets dataset that is presented in the [Fine-grained POS Tagging of German Tweets](https://pdfs.semanticscholar.org/82c9/90aa15e2e35de8294b4a721785da1ede20d0.pdf) paper from Ines Rehbein. It achieves an accuracy of 92.88% on the development set and an accuracy of **93.16%** on the final test dataset. ## Training All training code is released in [this](https://github.com/stefan-it/flair-experiments/tree/master/pos-twitter-german) repository. The model architecture uses the training strategy as proposed in the original [Flair](https://aclanthology.org/C18-1139/) paper: German FastText embeddings and German Flair Embeddings are stacked and passed into a BiLSTM-CRF sequence labeler, achieving robost SOTA results on PoS Tagging of German Tweets. The full training log can be found [here](training.log). ## Demo: How to use in Flair ```python from flair.data import Sentence from flair.models import SequenceTagger model = SequenceTagger.load('flair/de-pos-fine-grained') sent = Sentence("@Sneeekas Ich nicht \o/", use_tokenizer=False) model.predict(sent) print(sent) ``` This yields the following output: ```text Sentence[4]: "@Sneeekas Ich nicht \o/" → ["@Sneeekas"/ADDRESS, "Ich"/PPER, "nicht"/PTKNEG, "\o/"/EMO] ```