from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("sartajbhuvaji/bert-named-entity-recognition")
model = AutoModelForTokenClassification.from_pretrained("sartajbhuvaji/bert-named-entity-recognition")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"

ner_results = nlp(example)
print(ner_results)

json

[
  {
    "end": 19,
    "entity": "B-PER",
    "index": 4,
    "score": 0.99633455,
    "start": 11,
    "word": "wolfgang"
  },
  {
    "end": 40,
    "entity": "B-LOC",
    "index": 9,
    "score": 0.9987465,
    "start": 34,
    "word": "berlin"
  }
]

Training Details

Dataset : eriktks/conll2003

Abbreviation	Description
O	Outside of a named entity
B-MISC	Beginning of a miscellaneous entity right after another miscellaneous entity
I-MISC	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 organization right after another organization
I-ORG	Organization
B-LOC	Beginning of a location right after another location
I-LOC	Location

Training Procedure

Full Model Finetune
Epochs : 5

Training Loss Curves

Trainer

global_step: 4390
training_loss: 0.040937909830132485
train_runtime: 206.3611
train_samples_per_second: 340.205
train_steps_per_second: 21.273
total_flos: 1702317283240608.0
train_loss: 0.040937909830132485
epoch: 5.0

Evaluation

Precision: 0.8992
Recall: 0.9115
F1 Score: 0.9053

Classification Report

Class	Precision	Recall	F1-Score	Support
LOC	0.91	0.93	0.92	1668
MISC	0.76	0.81	0.78	702
ORG	0.87	0.88	0.88	1661
PER	0.98	0.97	0.97	1617
Micro Avg	0.90	0.91	0.91	5648
Macro Avg	0.88	0.90	0.89	5648
Weighted Avg	0.90	0.91	0.91	5648

Evaluation Dataset : eriktks/conll2003