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readme: add results for German and French HIPE-2020 datasets

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@@ -26,27 +26,30 @@ More details can be found in [our GitHub repository](https://github.com/stefan-i
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  We test our pretrained language models on various datasets from HIPE-2020, HIPE-2022 and Europeana. The following table
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  shows an overview of used datasets.
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- | Language | Datasets
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- |----------|----------------------------------------------------|
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- | English | [AjMC] - [TopRes19th] |
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- | German | [AjMC] - [NewsEye] |
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- | French | [AjMC] - [ICDAR-Europeana] - [LeTemps] - [NewsEye] |
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- | Finnish | [NewsEye] |
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- | Swedish | [NewsEye] |
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- | Dutch | [ICDAR-Europeana] |
 
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  [AjMC]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md
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  [NewsEye]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md
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  [TopRes19th]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md
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  [ICDAR-Europeana]: https://github.com/stefan-it/historic-domain-adaptation-icdar
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  [LeTemps]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md
 
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  Results:
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- | Model | English AjMC | German AjMC | French AjMC | German NewsEye | French NewsEye | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR | French ICDAR | French LeTemps | English TopRes19th | Avg. |
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- |---------------------------------------------------------------------------|--------------|--------------|--------------|----------------|----------------|-----------------|-----------------|--------------|--------------|----------------|--------------------|-----------|
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- | hmBERT (32k) [Schweter et al.](https://ceur-ws.org/Vol-3180/paper-87.pdf) | 85.36 ± 0.94 | 89.08 ± 0.09 | 85.10 ± 0.60 | 39.65 ± 1.01 | 81.47 ± 0.36 | 77.28 ± 0.37 | 82.85 ± 0.83 | 82.11 ± 0.61 | 77.21 ± 0.16 | 65.73 ± 0.56 | 80.94 ± 0.86 | 76.98 |
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- | hmTEAMS (Ours) | 86.41 ± 0.36 | 88.64 ± 0.42 | 85.41 ± 0.67 | 41.51 ± 2.82 | 83.20 ± 0.79 | 79.27 ± 1.88 | 82.78 ± 0.60 | 88.21 ± 0.39 | 78.03 ± 0.39 | 66.71 ± 0.46 | 81.36 ± 0.59 | **78.32** |
 
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  # Acknowledgements
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  We test our pretrained language models on various datasets from HIPE-2020, HIPE-2022 and Europeana. The following table
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  shows an overview of used datasets.
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+
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+ | Language | Datasets |
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+ |----------|------------------------------------------------------------------|
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+ | English | [AjMC] - [TopRes19th] |
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+ | German | [AjMC] - [NewsEye] - [HIPE-2020] |
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+ | French | [AjMC] - [ICDAR-Europeana] - [LeTemps] - [NewsEye] - [HIPE-2020] |
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+ | Finnish | [NewsEye] |
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+ | Swedish | [NewsEye] |
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+ | Dutch | [ICDAR-Europeana] |
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  [AjMC]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md
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  [NewsEye]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md
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  [TopRes19th]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md
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  [ICDAR-Europeana]: https://github.com/stefan-it/historic-domain-adaptation-icdar
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  [LeTemps]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md
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+ [HIPE-2020]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-hipe2020.md
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  Results:
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+
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+ | Model | English AjMC | German AjMC | French AjMC | German NewsEye | French NewsEye | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR | French ICDAR | French LeTemps | English TopRes19th | German HIPE-2020 | French HIPE-2020 | Avg. |
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+ |---------------------------------------------------------------------------|--------------|--------------|--------------|----------------|----------------|-----------------|-----------------|--------------|--------------|----------------|--------------------|------------------|------------------|-----------|
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+ | hmBERT (32k) [Schweter et al.](https://ceur-ws.org/Vol-3180/paper-87.pdf) | 85.36 ± 0.94 | 89.08 ± 0.09 | 85.10 ± 0.60 | 39.65 ± 1.01 | 81.47 ± 0.36 | 77.28 ± 0.37 | 82.85 ± 0.83 | 82.11 ± 0.61 | 77.21 ± 0.16 | 65.73 ± 0.56 | 80.94 ± 0.86 | 79.18 ± 0.38 | 83.47 ± 0.80 | 77.65 |
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+ | hmTEAMS (Ours) | 86.41 ± 0.36 | 88.64 ± 0.42 | 85.41 ± 0.67 | 41.51 ± 2.82 | 83.20 ± 0.79 | 79.27 ± 1.88 | 82.78 ± 0.60 | 88.21 ± 0.39 | 78.03 ± 0.39 | 66.71 ± 0.46 | 81.36 ± 0.59 | 80.15 ± 0.60 | 86.07 ± 0.49 | **79.06** |
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  # Acknowledgements
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