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de-pos-fine-grained
flair/de-pos-fine-grained
de-pos-fine-grained is a machine learning model from flair. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for flair.
This Flair model was trained on the German Tweets dataset that is presented in the Fine-grained POS Tagging of German Tweets paper from Ines Rehbein.
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At a glance
- Library
- flair
- Access
- Public
- Created
- May 15, 2023
- Updated
- Mar 12, 2025
- SHA
- 06416ddc
Model card
From the Hugging Face model README
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 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 repository.
The model architecture uses the training strategy as proposed in the original Flair 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.
Demo: How to use in Flair
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:
Sentence[4]: "@Sneeekas Ich nicht \o/" → ["@Sneeekas"/ADDRESS, "Ich"/PPER, "nicht"/PTKNEG, "\o/"/EMO]
- Library
- flair
- Languages
- de
- Created
- May 15, 2023
- Updated
- Mar 12, 2025