metadata
language:
- de
datasets:
- lattice-nlp/DROC
license:
- cc-by-nc-sa-4.0
base_model:
- LSX-UniWue/ModernGBERT_1B
base_model_relation: finetune
tags:
- NER
- token-classification
- named-entity-recognition
- digital-humanities
model-index:
- name: lattice-nlp/NER_DROC_ModernGBERT_1B
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: DROC
type: DROC
split: test
metrics:
- name: Micro Precision
type: precision
value: 0.9632986955560469
verified: true
- name: Micro Recall
type: recall
value: 0.9697306921878478
verified: true
- name: Micro F1
type: f1
value: 0.9665039929015085
verified: true
lattice-nlp/NER_DROC_ModernGBERT_1B
Model description
NER_DROC_ModernGBERT_1B is a fine-tuned version of LSX-UniWue/ModernGBERT_1B.
It is trained for Named Entity Recognition (NER) on German literary texts using the DROC dataset. The model performs span-based entity extraction, predicting entity spans directly rather than BIO token tags.
Intended use
This model is intended for:
- Named Entity Recognition in literary texts
- Digital humanities research (literary character analysis)
- Information extraction from historical novels
Performance
Performance is evaluated using exact span matching, on 8 held-out documents.
| Label | Precision | Recall | F1 | TP | FP | FN | Support | Support_% |
|---|---|---|---|---|---|---|---|---|
| PER | 0.9633 | 0.9697 | 0.9665 | 4357 | 166 | 136 | 4493 | 100.0 |
| MICRO | 0.9633 | 0.9697 | 0.9665 | - | - | - | 4493 | 100.0 |
| MACRO | - | - | 0.9665 | - | - | - | - | - |