from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
from peft import PeftModel

base = "bert-base-cased"
adapter = "starkdv123/conll2003-bert-ner-lora"

tok = AutoTokenizer.from_pretrained(base)
base_model = AutoModelForTokenClassification.from_pretrained(base, num_labels=9)
model = PeftModel.from_pretrained(base_model, adapter)

clf = pipeline("token-classification", model=model, tokenizer=tok, aggregation_strategy="simple")
clf("Chris Hoiles hit his 22nd homer for Baltimore.")

Training summary

LoRA: r=8, alpha=16, dropout=0.1
Targets: [query, key, value, output.dense]
Epochs: 3, LR: 2e-4, warmup 0.1, batch 16/32

Confusion Matrix

         LOC    MISC       O     ORG     PER
   LOC    384       6      35      43       5
  MISC     12    2138      80     100      33
     O     57     119   38060      58      21
   ORG     43     109      36    2304      11
   PER      1      27      18      22    2705