# 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,
{"language": ["la"], "library_name": "spacy", "metrics": ["accuracy"], "pipeline_tag": "token-classification"}
spacy token-classification la region:us
TAGS
#spacy #token-classification #la #region-us 

# NER for Latin

Trained using letters from the Bullinger collection, based on mbert.

# How to use


# Evaluation
- F-Score: 0.8970679975
- Precision: 0.8860135551,
- Recall: 0.9084017688,
# NER for Latin

Trained using letters from the Bullinger collection, based on mbert. # How to use # Evaluation
- F-Score: 0.8970679975
- Precision: 0.8860135551,
- Recall: 0.9084017688,
TAGS
#spacy #token-classification #la #region-us 
 # NER for Latin

Trained using letters from the Bullinger collection, based on mbert. # How to use # Evaluation
- F-Score: 0.8970679975
- Precision: 0.8860135551,
- Recall: 0.9084017688,
TAGS
#spacy #token-classification #la #region-us 
# NER for Latin

Trained using letters from the Bullinger collection, based on mbert.# How to use# Evaluation
- F-Score: 0.8970679975
- Precision: 0.8860135551,
- Recall: 0.9084017688,