which is entirely based on that task. If you would like to fine-tune a model on an NER task, you may leverage the
run_ner.py script.
Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as
belonging to one of 9 classes:
O, Outside of a named entity
B-MIS, Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS, Miscellaneous entity
B-PER, Beginning of a person's name right after another person's name
I-PER, Person's name
B-ORG, Beginning of an organisation right after another organisation
I-ORG, Organisation
B-LOC, Beginning of a location right after another location
I-LOC, Location
It leverages a fine-tuned model on CoNLL-2003, fine-tuned by @stefan-it from dbmdz.
```py
from transformers import pipeline
ner_pipe = pipeline("ner")
