| | --- |
| | language: |
| | - la |
| | metrics: |
| | - accuracy |
| | library_name: spacy |
| | pipeline_tag: token-classification |
| | --- |
| | # NER for Latin |
| |
|
| | Trained using letters from the [Bullinger collection](http://www.bullinger-digital.ch/), based on mbert. |
| |
|
| | # How to use |
| | ```python |
| | import spacy |
| | nlp = spacy.load('./enhg_pipeline') |
| | doc = nlp('Norimberga in proximum quoddam Ulmensibus oppidulum Leypphaim sese contulit, certa spe recuperandae sedis, e qua nuper est detrusus.') |
| | for ent in doc.ents: |
| | print(ent.text, ent.label_) |
| | |
| | # Output: |
| | # Norimberga GEO |
| | # Ulmensibus GEO |
| | ``` |
| |
|
| | # Evaluation |
| | - F-Score: 0.8970679975 |
| | - Precision: 0.8860135551, |
| | - Recall: 0.9084017688, |
