mpr_bert_german_ner

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This token-classification model aims to perform Named Entity Recognition on German-Austrian historical documents.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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

• Named Entity Recignition

Direct Use

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))

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!--### Downstream Use [optional] --> <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> <!-- [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> <!-- [More Information Needed] <!-- ### Recommendations --> <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> <!-- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. <!-- ## How to Get Started with the Model Use the code below to get started with the model. --> <!-- [More Information Needed] -->

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

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

<!--### Training Procedure --> <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> <!--#### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> <!--#### Speeds, Sizes, Times [optional] --> <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> <!-- [More Information Needed] --> <!--## Evaluation --> <!-- This section describes the evaluation protocols and provides the results. --> <!--### Testing Data, Factors & Metrics --> <!--#### Testing Data --> <!-- This should link to a Dataset Card if possible. --> <!-- [More Information Needed] --> <!--#### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> <!-- [More Information Needed] --> <!--#### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> <!-- [More Information Needed] --> <!--### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> <!-- [More Information Needed] --> <!--## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> <!--Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> <!--**BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> <!-- [More Information Needed] --> <!--## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]