
# 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)


{"language": "de"}
transformers pytorch jax bert fill-mask de arxiv:1508.01991 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #fill-mask #de #arxiv-1508.01991 #autotrain_compatible #endpoints_compatible #region-us 

Model description
=================


Dataset
-------


Trained on fictional and non-fictional German texts written between 1840 and 1920:


* Narrative texts from Digitale Bibliothek (URL
* Fairy tales and sagas from Grimm Korpus (URL
* Newspaper and magazine article from Mannheimer Korpus Historischer Zeitungen und Zeitschriften (URL
* Magazine article from the journal „Die Grenzboten“ (URL
* Fictional and non-fictional texts from Projekt Gutenberg (URL)


Hardware used
-------------


1 Tesla P4 GPU


Hyperparameters
---------------



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 URL


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)).


Hyperparameters



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.



Intended use:
-------------


Historical German Texts (1840 to 1920)


(Showed good performance with modern German fictional texts as well)



TAGS
#transformers #pytorch #jax #bert #fill-mask #de #arxiv-1508.01991 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #jax #bert #fill-mask #de #arxiv-1508.01991 #autotrain_compatible #endpoints_compatible #region-us 
