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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # hmBERT
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+
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+ Historical Multilingual Language Models for Named Entity Recognition. The following languages are covered by hmBERT:
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+
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+ * English (British Library Corpus - Books)
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+ * German (Europeana Newspaper)
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+ * French (Europeana Newspaper)
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+ * Finnish (Europeana Newspaper)
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+ * Swedish (Europeana Newspaper)
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+
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+ More details can be found in [our GitHub repository](https://github.com/dbmdz/clef-hipe) and in our
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+ [hmBERT paper](https://ceur-ws.org/Vol-3180/paper-87.pdf).
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+
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+ # Leaderboard
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+
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+ We test our pretrained language models on various datasets from HIPE-2020, HIPE-2022 and Europeana.
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+ The following table 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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+
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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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+
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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](https://huggingface.co/hmteams) | 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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+
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+ # Acknowledgements
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+
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+ We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
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+ [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.
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+
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+ Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
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+ Many Thanks for providing access to the TPUs ❤️