NER model for definition component recognition in German scientific texts

xlm-roberta-base-definitions_ner is a NER model (token classification) in the scientific domain in German, finetuned from the model xlm-roberta-base. It was trained using a custom annotated dataset of around 10,000 training and 2,000 test examples containing definition- and non-definition-related sentences from wikipedia articles in german.

The model is specifically designed to recognize and classify components of definitions, using the following entity labels:

DF: Definiendum (the term being defined)
VF: Definitor (the verb or phrase introducing the definition)
GF: Definiens (the explanation or meaning)

Training was conducted using a standard NER objective. The model achieves an F1 score of approximately 83% on the evaluation set.

Here are the overall final metrics on the test dataset after 5 epochs of training:

f1: 0.8262004492199356
precision: 0.8189914550487424
recall: 0.8335374816266536
loss: 0.312337189912796

Model Performance Comparision on wiki_definitions_de_multitask:

Model	Precision	Recall	F1 Score	Eval Samples per Second	Epoch
distilbert-base-multilingual-cased-definitions_ner	80.76	81.74	81.25	457.53	5.0
scibert_scivocab_cased-definitions_ner	80.54	82.11	81.32	236.61	4.0
GottBERT_base_best-definitions_ner	82.98	82.81	82.90	272.26	5.0
xlm-roberta-base-definitions_ner	81.90	83.35	82.62	241.21	5.0
gbert-base-definitions_ner	82.73	83.56	83.14	278.87	5.0
gbert-large-definitions_ner	80.67	83.36	81.99	109.83	2.0