Create README.md

README.md

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+---
+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,