README.md

---
license: cc-by-4.0
language:
- de
base_model:
- google-bert/bert-base-cased
pipeline_tag: token-classification
tags:
- ner
- german
- historical_texts
- history
- deutsch
---
# Model Card for German-Austrian Historical NER

<!-- Provide a quick summary of what the model is/does. -->

This token-classification model  aims to perform Named Entity Recognition on German-Austrian historical documents.
<br>The model has been trained using the tagged entities 10319 samples provided by https://nerdpool-api.acdh-dev.oeaw.ac.at/.
<br>The model has been trained to identify entities from the Minutes of the Austian Council of Ministries.


- **Developed by:** Dimitra Grigoriou
- **Shared by**: Dimitra Grigoriou
- **Model type:** token classification 
- **Language(s) (NLP):** German, Austrian German
- **License:** CC By-4.0
- **Finetuned from model :** google-bert-case

## Uses

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- Named Entity Recignition
### Direct Use 

  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

  model = AutoModelForTokenClassification.from_pretrained("demigrigo/mpr_bert_german_ner")
  tokenizer = AutoTokenizer.from_pretrained("demigrigo/mpr_bert_german_ner")
  
  nlp = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="average") 
  
  text = "Ernennung FML. Peter Zaninis zum Kriegsminister" ##example sentence
  print(nlp(text))
  ```



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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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## Training Details

### Training Data

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Training data from: https://nerdpool-api.acdh-dev.oeaw.ac.at/
<br>The data transformed into BIO tagging style required by the original model.

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#### Training Hyperparameters

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#### Summary



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## Technical Specifications [optional]

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