metadata
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8379052369
- name: NER Recall
type: recall
value: 0.8704663212
- name: NER F Score
type: f_score
value: 0.8538754765
Introduction
German Named Entity Recognition model for recognizing Bavarian landmarks. Fine-tuned "bert-base-german-cased" with 6450 annotated sentences of which 1467 contained landmarks, from subtitles of videos from Bayerischer Rundfunk.
| Feature | Description |
|---|---|
| Name | de_pipeline |
| Version | 0.1.0 |
| spaCy | >=3.3.0,<3.4.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Constantin Förster |
Label Scheme
View label scheme (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LM |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
85.39 |
ENTS_P |
83.79 |
ENTS_R |
87.05 |
TRANSFORMER_LOSS |
4216.96 |
NER_LOSS |
78511.31 |