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bert-base-historical-german-rw-cased
redewiedergabe/bert-base-historical-german-rw-cased
bert-base-historical-german-rw-cased is a fill-mask model from redewiedergabe. Use it when you need the model to fill a missing word. It is set up for transformers.
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://ww…
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- May 20, 2021
- SHA
- 011b11dc
Model card
From the Hugging Face model README
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) which implements a BiLSTM-CRF architecture on top of a language embedding (as proposed by Huang et al. (2015)).
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)
- Task
- Fill-Mask
- Library
- transformers
- Type
- bert
- Languages
- de
- Created
- Mar 2, 2022
- Updated
- May 20, 2021