readme: ---
  language: de
  ---
  
  # 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)
  
  