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README.md

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+---
+language: gmh
+library_name: flair
+license: cc-by-sa-4.0
+tags:
+- flair
+- embeddings
+- contextual-string-embeddings
+- middle-high-german
+- medieval-german
+- digital-humanities
+- mhdbdb
+widget:
+- text: "von abegescheidenheit ich hân der geschrift vil gelesen"
+---
+
+# Middle High German Contextual String Embeddings (Forward)
+
+This model provides contextual string embeddings for Middle High German (MHG), trained on literary and historical texts as part of the digital humanities research surrounding medieval Germanic languages. 
+
+It is optimized for **Middle High German corpora** and serves as a robust general-purpose embedding for downstream NLP tasks involving medieval German.
+
+## Data provenance and acknowledgment
+The model was trained on on open data prepared by Mittelhochdeutsche Begriffsdatenbank (MHDBDB). Universität Salzburg. Koordination: Katharina Zeppezauer-Wachauer. Seit 1992. URL: [http://www.mhdbdb.plus.ac.at/](http://www.mhdbdb.plus.ac.at/). DOI: 10.60646/MHDBDB).
+
+## Model Description
+- **Architecture:** Character-level LSTM (Flair Language Model)
+- **Direction:** Forward
+- **Data Source:** TEI-encoded texts from the [Middle High German Conceptual Database](https://github.com/Middle-High-German-Conceptual-Database/TEI-Texte) (MHDBDB).
+- **Training Epochs:** 30
+- **Final Perplexity:** 7.34
+- **Final Validation Loss:** 1.9934
+
+## Usage
+To use this model in Flair, install the library (`pip install flair`) and load the model directly from the Hub. Note that for best results in downstream tasks like NER or Part-of-Speech tagging, it is recommended to use this forward model in combination with a corresponding backward model (`mschonhardt/mdh-mhdbdb-backward`).
+
+```python
+from flair.embeddings import FlairEmbeddings, StackedEmbeddings
+
+# Load the forward model
+forward_embeddings = FlairEmbeddings('mschonhardt/mdh-mhdbdb-forward')
+
+# Load the backward model
+backward_embeddings = FlairEmbeddings('mschonhardt/mdh-mhdbdb-backward')
+
+# Stack them for best performance
+stacked_embeddings = StackedEmbeddings([forward_embeddings, backward_embeddings])
+
+# Example usage
+from flair.data import Sentence
+sentence = Sentence("von abegescheidenheit ich hân der geschrift vil gelesen")
+forward_embeddings.embed(sentence)
+```
+
+## Citation
+If you use this model, please cite the original research paper as well as the model source.
+
+```bibtex
+@software{schonhardt_michael_2026_mhg_flair,
+  author       = "Schonhardt, Michael",
+  title        = "Middle High German Contextual String Embeddings (Forward): Trained on the MHDBDB TEI-Texte Corpus",
+  year         = 2026,
+  publisher    = "Zenodo",
+  doi="18657493",
+  url="https://doi.org/10.5281/zenodo.18657493"
+}
+```
+
+```bibtex
+@inproceedings{akbik-etal-2018-contextual,
+    title = "Contextual String Embeddings for Sequence Labeling",
+    author = "Akbik, Alan and
+      Blythe, Duncan and
+      Vollgraf, Roland",
+    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
+    year = "2018",
+    url = "[https://aclanthology.org/C18-1139/](https://aclanthology.org/C18-1139/)",
+    pages = "1638--1649"
+}
+```

mhd-forward.pt

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training.log

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