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language: german
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# Model description
## Dataset
Trained on fictional and non-fictional German texts written between 1840 and 1920:
* Narrative texts from Digitale Bibliothek (https://textgrid.de/digitale-bibliothek)
* Fairy tales and sagas from Grimm Korpus (https://www1.ids-mannheim.de/kl/projekte/korpora/archiv/gri.html)
* Newspaper and magazine article from Mannheimer Korpus Historischer Zeitungen und Zeitschriften (https://repos.ids-mannheim.de/mkhz-beschreibung.html)
* Magazine article from the journal „Die Grenzboten“ (http://www.deutschestextarchiv.de/doku/textquellen#grenzboten)
* Fictional and non-fictional texts from Projekt Gutenberg (https://www.projekt-gutenberg.org)
## Hardware used
1 Tesla P4 GPU
## Hyperparameters
| Parameter | Value |
|-------------------------------|----------|
| Epochs | 3 |
| Gradient_accumulation_steps | 1 |
| Train_batch_size | 32 |
| Learning_rate | 0.00003 |
| Max_seq_len | 128 |
## Evaluation results: Automatic tagging of four forms of speech/thought/writing representation in historical fictional and non-fictional German texts
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](https://www.aclweb.org/anthology/N19-4010)) which implements a BiLSTM-CRF architecture on top of a language embedding (as proposed by [Huang et al. (2015)](https://arxiv.org/abs/1508.01991)).
Hyperparameters
| Parameter | Value |
|-------------------------------|------------|
| Hidden_size | 256 |
| Learning_rate | 0.1 |
| Mini_batch_size | 8 |
| Max_epochs | 150 |
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.
| | BERT ||| FastText+Flair |||Test data|
|----------------|----------|-----------|----------|------|-----------|--------|--------|
| | F1 | Precision | Recall | F1 | Precision | Recall ||
| 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|
## Intended use:
Historical German Texts (1840 to 1920)
(Showed good performance with modern German fictional texts as well)