Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/redewiedergabe/bert-base-historical-german-rw-cased/README.md
README.md
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---
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language: de
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---
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# Model description
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## Dataset
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Trained on fictional and non-fictional German texts written between 1840 and 1920:
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* Narrative texts from Digitale Bibliothek (https://textgrid.de/digitale-bibliothek)
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* Fairy tales and sagas from Grimm Korpus (https://www1.ids-mannheim.de/kl/projekte/korpora/archiv/gri.html)
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* Newspaper and magazine article from Mannheimer Korpus Historischer Zeitungen und Zeitschriften (https://repos.ids-mannheim.de/mkhz-beschreibung.html)
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* Magazine article from the journal „Die Grenzboten“ (http://www.deutschestextarchiv.de/doku/textquellen#grenzboten)
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* Fictional and non-fictional texts from Projekt Gutenberg (https://www.projekt-gutenberg.org)
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## Hardware used
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1 Tesla P4 GPU
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## Hyperparameters
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| Parameter | Value |
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|-------------------------------|----------|
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| Epochs | 3 |
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| Gradient_accumulation_steps | 1 |
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| Train_batch_size | 32 |
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| Learning_rate | 0.00003 |
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| Max_seq_len | 128 |
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## Evaluation results: Automatic tagging of four forms of speech/thought/writing representation in historical fictional and non-fictional German texts
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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.
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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)).
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Hyperparameters
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| Parameter | Value |
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|-------------------------------|------------|
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| Hidden_size | 256 |
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| Learning_rate | 0.1 |
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| Mini_batch_size | 8 |
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| Max_epochs | 150 |
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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.
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| | BERT ||| FastText+Flair |||Test data|
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|----------------|----------|-----------|----------|------|-----------|--------|--------|
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| | F1 | Precision | Recall | F1 | Precision | Recall ||
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| Direct | 0.80 | 0.86 | 0.74 | 0.84 | 0.90 | 0.79 |historical German, fictional & non-fictional|
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| Indirect | **0.76** | **0.79** | **0.73** | 0.73 | 0.78 | 0.68 |historical German, fictional & non-fictional|
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| Reported | **0.58** | **0.69** | **0.51** | 0.56 | 0.68 | 0.48 |historical German, fictional & non-fictional|
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| Free indirect | **0.57** | **0.80** | **0.44** | 0.47 | 0.78 | 0.34 |modern German, fictional|
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## Intended use:
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Historical German Texts (1840 to 1920)
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(Showed good performance with modern German fictional texts as well)
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