From 81484b447b7d8504ff5e1cfff38ec35918383963 Mon Sep 17 00:00:00 2001
From: redewiedergabe <47349182+redewiedergabe@users.noreply.github.com>
Date: Fri, 3 Apr 2020 03:48:31 +0200
Subject: [PATCH] Create README.md (#3568)

* Create README.md

* added meta block (language: german)

* Added additional information about test data
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+---
+language: german
+---
+
+# 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)
+