How to use this model directly from the
tokenizer = AutoTokenizer.from_pretrained("redewiedergabe/bert-base-historical-german-rw-cased") model = AutoModelWithLMHead.from_pretrained("redewiedergabe/bert-base-historical-german-rw-cased")
Trained on fictional and non-fictional German texts written between 1840 and 1920:
1 Tesla P4 GPU
The language model was used in the task to tag direct, indirect, reported and free indirect speech/thought/writing representation in fictional and non-fictional German texts. The tagger is available and described in detail at https://github.com/redewiedergabe/tagger.
The tagging model was trained using the SequenceTagger Class of the Flair framework (Akbik et al., 2019) which implements a BiLSTM-CRF architecture on top of a language embedding (as proposed by Huang et al. (2015)).
Results are reported below in comparison to a custom trained flair embedding, which was stacked onto a custom trained fastText-model. Both models were trained on the same dataset.
|Direct||0.80||0.86||0.74||0.84||0.90||0.79||historical German, fictional & non-fictional|
|Indirect||0.76||0.79||0.73||0.73||0.78||0.68||historical German, fictional & non-fictional|
|Reported||0.58||0.69||0.51||0.56||0.68||0.48||historical German, fictional & non-fictional|
|Free indirect||0.57||0.80||0.44||0.47||0.78||0.34||modern German, fictional|
Historical German Texts (1840 to 1920)
(Showed good performance with modern German fictional texts as well)